{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mplfinance in d:\\computer\\anaconda\\lib\\site-packages (0.12.10b0)\n",
      "Requirement already satisfied: matplotlib in d:\\computer\\anaconda\\lib\\site-packages (from mplfinance) (3.8.4)\n",
      "Requirement already satisfied: pandas in d:\\computer\\anaconda\\lib\\site-packages (from mplfinance) (2.2.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in d:\\computer\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\computer\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in d:\\computer\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in d:\\computer\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.21 in d:\\computer\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (1.26.4)\n",
      "Requirement already satisfied: packaging>=20.0 in d:\\computer\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in d:\\computer\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in d:\\computer\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in d:\\computer\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in d:\\computer\\anaconda\\lib\\site-packages (from pandas->mplfinance) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in d:\\computer\\anaconda\\lib\\site-packages (from pandas->mplfinance) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in d:\\computer\\anaconda\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->mplfinance) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install mplfinance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tushare in d:\\computer\\anaconda\\lib\\site-packages (1.4.14)\n",
      "Requirement already satisfied: pandas in d:\\computer\\anaconda\\lib\\site-packages (from tushare) (2.2.2)\n",
      "Requirement already satisfied: requests in d:\\computer\\anaconda\\lib\\site-packages (from tushare) (2.32.2)\n",
      "Requirement already satisfied: lxml in d:\\computer\\anaconda\\lib\\site-packages (from tushare) (5.2.1)\n",
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      "Requirement already satisfied: bs4 in d:\\computer\\anaconda\\lib\\site-packages (from tushare) (0.0.2)\n",
      "Requirement already satisfied: websocket-client>=0.57.0 in d:\\computer\\anaconda\\lib\\site-packages (from tushare) (1.8.0)\n",
      "Requirement already satisfied: tqdm in d:\\computer\\anaconda\\lib\\site-packages (from tushare) (4.66.4)\n",
      "Requirement already satisfied: beautifulsoup4 in d:\\computer\\anaconda\\lib\\site-packages (from bs4->tushare) (4.12.3)\n",
      "Requirement already satisfied: numpy>=1.26.0 in d:\\computer\\anaconda\\lib\\site-packages (from pandas->tushare) (1.26.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in d:\\computer\\anaconda\\lib\\site-packages (from pandas->tushare) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in d:\\computer\\anaconda\\lib\\site-packages (from pandas->tushare) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in d:\\computer\\anaconda\\lib\\site-packages (from pandas->tushare) (2023.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in d:\\computer\\anaconda\\lib\\site-packages (from requests->tushare) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in d:\\computer\\anaconda\\lib\\site-packages (from requests->tushare) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in d:\\computer\\anaconda\\lib\\site-packages (from requests->tushare) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in d:\\computer\\anaconda\\lib\\site-packages (from requests->tushare) (2024.7.4)\n",
      "Requirement already satisfied: colorama in d:\\computer\\anaconda\\lib\\site-packages (from tqdm->tushare) (0.4.6)\n",
      "Requirement already satisfied: six>=1.5 in d:\\computer\\anaconda\\lib\\site-packages (from python-dateutil>=2.8.2->pandas->tushare) (1.16.0)\n",
      "Requirement already satisfied: soupsieve>1.2 in d:\\computer\\anaconda\\lib\\site-packages (from beautifulsoup4->bs4->tushare) (2.5)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tushare\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in d:\\computer\\anaconda\\lib\\site-packages (2.18.0)\n",
      "Requirement already satisfied: tensorflow-intel==2.18.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow) (2.18.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (2.1.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers>=24.3.25 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (24.12.23)\n",
      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (0.6.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (0.2.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (18.1.1)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (3.4.0)\n",
      "Requirement already satisfied: packaging in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (23.2)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (3.20.3)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (2.32.2)\n",
      "Requirement already satisfied: setuptools in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (69.5.1)\n",
      "Requirement already satisfied: six>=1.12.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (1.16.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (2.5.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (4.11.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (1.14.1)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (1.68.1)\n",
      "Requirement already satisfied: tensorboard<2.19,>=2.18 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (2.18.0)\n",
      "Requirement already satisfied: keras>=3.5.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (3.7.0)\n",
      "Requirement already satisfied: numpy<2.1.0,>=1.26.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (1.26.4)\n",
      "Requirement already satisfied: h5py>=3.11.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (3.11.0)\n",
      "Requirement already satisfied: ml-dtypes<0.5.0,>=0.4.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (0.4.1)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in d:\\computer\\anaconda\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.18.0->tensorflow) (0.43.0)\n",
      "Requirement already satisfied: rich in d:\\computer\\anaconda\\lib\\site-packages (from keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (13.3.5)\n",
      "Requirement already satisfied: namex in d:\\computer\\anaconda\\lib\\site-packages (from keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (0.0.8)\n",
      "Requirement already satisfied: optree in d:\\computer\\anaconda\\lib\\site-packages (from keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (0.13.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in d:\\computer\\anaconda\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.18.0->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in d:\\computer\\anaconda\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.18.0->tensorflow) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in d:\\computer\\anaconda\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.18.0->tensorflow) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in d:\\computer\\anaconda\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.18.0->tensorflow) (2024.7.4)\n",
      "Requirement already satisfied: markdown>=2.6.8 in d:\\computer\\anaconda\\lib\\site-packages (from tensorboard<2.19,>=2.18->tensorflow-intel==2.18.0->tensorflow) (3.4.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in d:\\computer\\anaconda\\lib\\site-packages (from tensorboard<2.19,>=2.18->tensorflow-intel==2.18.0->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in d:\\computer\\anaconda\\lib\\site-packages (from tensorboard<2.19,>=2.18->tensorflow-intel==2.18.0->tensorflow) (3.0.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in d:\\computer\\anaconda\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.19,>=2.18->tensorflow-intel==2.18.0->tensorflow) (2.1.3)\n",
      "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in d:\\computer\\anaconda\\lib\\site-packages (from rich->keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (2.2.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in d:\\computer\\anaconda\\lib\\site-packages (from rich->keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (2.15.1)\n",
      "Requirement already satisfied: mdurl~=0.1 in d:\\computer\\anaconda\\lib\\site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (0.1.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALLOW_THREADS',\n",
       " 'BUFSIZE',\n",
       " 'CLIP',\n",
       " 'DataSource',\n",
       " 'ERR_CALL',\n",
       " 'ERR_DEFAULT',\n",
       " 'ERR_IGNORE',\n",
       " 'ERR_LOG',\n",
       " 'ERR_PRINT',\n",
       " 'ERR_RAISE',\n",
       " 'ERR_WARN',\n",
       " 'FLOATING_POINT_SUPPORT',\n",
       " 'FPE_DIVIDEBYZERO',\n",
       " 'FPE_INVALID',\n",
       " 'FPE_OVERFLOW',\n",
       " 'FPE_UNDERFLOW',\n",
       " 'False_',\n",
       " 'Inf',\n",
       " 'Infinity',\n",
       " 'MAXDIMS',\n",
       " 'MAY_SHARE_BOUNDS',\n",
       " 'MAY_SHARE_EXACT',\n",
       " 'NAN',\n",
       " 'NINF',\n",
       " 'NZERO',\n",
       " 'NaN',\n",
       " 'PINF',\n",
       " 'PZERO',\n",
       " 'RAISE',\n",
       " 'RankWarning',\n",
       " 'SHIFT_DIVIDEBYZERO',\n",
       " 'SHIFT_INVALID',\n",
       " 'SHIFT_OVERFLOW',\n",
       " 'SHIFT_UNDERFLOW',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " 'UFUNC_BUFSIZE_DEFAULT',\n",
       " 'UFUNC_PYVALS_NAME',\n",
       " 'WRAP',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '_UFUNC_API',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__deprecated_attrs__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_functions__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_add_newdoc_ufunc',\n",
       " '_builtins',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_financial_names',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_typing',\n",
       " '_using_numpy2_behavior',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'add',\n",
       " 'add_docstring',\n",
       " 'add_newdoc',\n",
       " 'add_newdoc_ufunc',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'alltrue',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfarray',\n",
       " 'asfortranarray',\n",
       " 'asmatrix',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'byte_bounds',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'cfloat',\n",
       " 'char',\n",
       " 'character',\n",
       " 'chararray',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'clongfloat',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'compare_chararrays',\n",
       " 'compat',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complex_',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumproduct',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'deprecate',\n",
       " 'deprecate_with_doc',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'disp',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'expm1x',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'fabs',\n",
       " 'fastCopyAndTranspose',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'find_common_type',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'format_parser',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_array_wrap',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'geterrobj',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'infty',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issctype',\n",
       " 'issubclass_',\n",
       " 'issubdtype',\n",
       " 'issubsctype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'longcomplex',\n",
       " 'longdouble',\n",
       " 'longfloat',\n",
       " 'longlong',\n",
       " 'lookfor',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'mat',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'maximum_sctype',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'msort',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'nbytes',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'numarray',\n",
       " 'number',\n",
       " 'obj2sctype',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'oldnumeric',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'recfromcsv',\n",
       " 'recfromtxt',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'safe_eval',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctype2char',\n",
       " 'sctypeDict',\n",
       " 'sctypes',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_numeric_ops',\n",
       " 'set_printoptions',\n",
       " 'set_string_function',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'string_',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000012.SZ   20241225   5.40   5.42   5.32   5.35       5.40   -0.05   \n",
      "1     000012.SZ   20241224   5.31   5.41   5.31   5.40       5.31    0.09   \n",
      "2     000012.SZ   20241223   5.38   5.40   5.30   5.31       5.37   -0.06   \n",
      "3     000012.SZ   20241220   5.41   5.42   5.37   5.37       5.41   -0.04   \n",
      "4     000012.SZ   20241219   5.41   5.42   5.33   5.41       5.43   -0.02   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3589  000012.SZ   20100108  18.63  18.69  18.19  18.43      18.63   -0.20   \n",
      "3590  000012.SZ   20100107  19.50  19.64  18.00  18.63      19.67   -1.04   \n",
      "3591  000012.SZ   20100106  19.04  19.68  18.91  19.67      19.08    0.59   \n",
      "3592  000012.SZ   20100105  19.33  19.50  18.90  19.08      19.41   -0.33   \n",
      "3593  000012.SZ   20100104  19.79  19.80  19.36  19.41      19.60   -0.19   \n",
      "\n",
      "      pct_chg        vol       amount  \n",
      "0     -0.9259  112381.90   60072.3160  \n",
      "1      1.6949  129886.92   69849.2380  \n",
      "2     -1.1173  166449.73   88952.4870  \n",
      "3     -0.7394  127785.89   68906.9310  \n",
      "4     -0.3683  151824.59   81634.2410  \n",
      "...       ...        ...          ...  \n",
      "3589  -1.0700   82173.51  150636.9317  \n",
      "3590  -5.2900  116935.61  222878.6388  \n",
      "3591   3.0900  138097.89  268039.4558  \n",
      "3592  -1.7000  101992.39  194401.6285  \n",
      "3593  -0.9700   88680.55  173666.3594  \n",
      "\n",
      "[3594 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('519f9f74949ebce4824e9c07e49e16150fb55f55e8ced4f32ca43fea')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"000012.SZ\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000012.SZ   20241225   5.40   5.42   5.32   5.35       5.40   -0.05   \n",
      "1     000012.SZ   20241224   5.31   5.41   5.31   5.40       5.31    0.09   \n",
      "2     000012.SZ   20241223   5.38   5.40   5.30   5.31       5.37   -0.06   \n",
      "3     000012.SZ   20241220   5.41   5.42   5.37   5.37       5.41   -0.04   \n",
      "4     000012.SZ   20241219   5.41   5.42   5.33   5.41       5.43   -0.02   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3589  000012.SZ   20100108  18.63  18.69  18.19  18.43      18.63   -0.20   \n",
      "3590  000012.SZ   20100107  19.50  19.64  18.00  18.63      19.67   -1.04   \n",
      "3591  000012.SZ   20100106  19.04  19.68  18.91  19.67      19.08    0.59   \n",
      "3592  000012.SZ   20100105  19.33  19.50  18.90  19.08      19.41   -0.33   \n",
      "3593  000012.SZ   20100104  19.79  19.80  19.36  19.41      19.60   -0.19   \n",
      "\n",
      "      pct_chg        vol       amount  \n",
      "0     -0.9259  112381.90   60072.3160  \n",
      "1      1.6949  129886.92   69849.2380  \n",
      "2     -1.1173  166449.73   88952.4870  \n",
      "3     -0.7394  127785.89   68906.9310  \n",
      "4     -0.3683  151824.59   81634.2410  \n",
      "...       ...        ...          ...  \n",
      "3589  -1.0700   82173.51  150636.9317  \n",
      "3590  -5.2900  116935.61  222878.6388  \n",
      "3591   3.0900  138097.89  268039.4558  \n",
      "3592  -1.7000  101992.39  194401.6285  \n",
      "3593  -0.9700   88680.55  173666.3594  \n",
      "\n",
      "[3594 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock601939.csv\")\n",
    "stockname='建设银行'\n",
    "stockcode='601939'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3584</th>\n",
       "      <th>3585</th>\n",
       "      <th>3586</th>\n",
       "      <th>3587</th>\n",
       "      <th>3588</th>\n",
       "      <th>3589</th>\n",
       "      <th>3590</th>\n",
       "      <th>3591</th>\n",
       "      <th>3592</th>\n",
       "      <th>3593</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "      <td>000012.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>5.4</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.48</td>\n",
       "      <td>5.56</td>\n",
       "      <td>5.61</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.64</td>\n",
       "      <td>...</td>\n",
       "      <td>18.53</td>\n",
       "      <td>18.21</td>\n",
       "      <td>18.43</td>\n",
       "      <td>18.16</td>\n",
       "      <td>18.78</td>\n",
       "      <td>18.63</td>\n",
       "      <td>19.5</td>\n",
       "      <td>19.04</td>\n",
       "      <td>19.33</td>\n",
       "      <td>19.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>5.42</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.52</td>\n",
       "      <td>5.58</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.78</td>\n",
       "      <td>5.76</td>\n",
       "      <td>...</td>\n",
       "      <td>19.55</td>\n",
       "      <td>18.58</td>\n",
       "      <td>18.69</td>\n",
       "      <td>18.92</td>\n",
       "      <td>18.78</td>\n",
       "      <td>18.69</td>\n",
       "      <td>19.64</td>\n",
       "      <td>19.68</td>\n",
       "      <td>19.5</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>5.32</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.53</td>\n",
       "      <td>5.64</td>\n",
       "      <td>5.58</td>\n",
       "      <td>...</td>\n",
       "      <td>18.41</td>\n",
       "      <td>18.09</td>\n",
       "      <td>18.09</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.19</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.91</td>\n",
       "      <td>18.9</td>\n",
       "      <td>19.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>5.35</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.43</td>\n",
       "      <td>5.45</td>\n",
       "      <td>5.56</td>\n",
       "      <td>5.64</td>\n",
       "      <td>5.74</td>\n",
       "      <td>...</td>\n",
       "      <td>19.32</td>\n",
       "      <td>18.53</td>\n",
       "      <td>18.13</td>\n",
       "      <td>18.85</td>\n",
       "      <td>18.11</td>\n",
       "      <td>18.43</td>\n",
       "      <td>18.63</td>\n",
       "      <td>19.67</td>\n",
       "      <td>19.08</td>\n",
       "      <td>19.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>5.4</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.43</td>\n",
       "      <td>5.45</td>\n",
       "      <td>5.56</td>\n",
       "      <td>5.64</td>\n",
       "      <td>5.74</td>\n",
       "      <td>5.64</td>\n",
       "      <td>...</td>\n",
       "      <td>18.53</td>\n",
       "      <td>18.13</td>\n",
       "      <td>18.85</td>\n",
       "      <td>18.11</td>\n",
       "      <td>18.43</td>\n",
       "      <td>18.63</td>\n",
       "      <td>19.67</td>\n",
       "      <td>19.08</td>\n",
       "      <td>19.41</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>0.59</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>-0.9259</td>\n",
       "      <td>1.6949</td>\n",
       "      <td>-1.1173</td>\n",
       "      <td>-0.7394</td>\n",
       "      <td>-0.3683</td>\n",
       "      <td>-0.367</td>\n",
       "      <td>-1.9784</td>\n",
       "      <td>-1.4184</td>\n",
       "      <td>-1.7422</td>\n",
       "      <td>1.773</td>\n",
       "      <td>...</td>\n",
       "      <td>4.26</td>\n",
       "      <td>2.21</td>\n",
       "      <td>-3.82</td>\n",
       "      <td>4.09</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-5.29</td>\n",
       "      <td>3.09</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>112381.9</td>\n",
       "      <td>129886.92</td>\n",
       "      <td>166449.73</td>\n",
       "      <td>127785.89</td>\n",
       "      <td>151824.59</td>\n",
       "      <td>138202.3</td>\n",
       "      <td>213145.79</td>\n",
       "      <td>247121.13</td>\n",
       "      <td>471299.41</td>\n",
       "      <td>434181.81</td>\n",
       "      <td>...</td>\n",
       "      <td>160030.34</td>\n",
       "      <td>69393.29</td>\n",
       "      <td>87698.17</td>\n",
       "      <td>82240.44</td>\n",
       "      <td>107746.62</td>\n",
       "      <td>82173.51</td>\n",
       "      <td>116935.61</td>\n",
       "      <td>138097.89</td>\n",
       "      <td>101992.39</td>\n",
       "      <td>88680.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>60072.316</td>\n",
       "      <td>69849.238</td>\n",
       "      <td>88952.487</td>\n",
       "      <td>68906.931</td>\n",
       "      <td>81634.241</td>\n",
       "      <td>75580.771</td>\n",
       "      <td>116813.336</td>\n",
       "      <td>138208.259</td>\n",
       "      <td>268798.53</td>\n",
       "      <td>247756.399</td>\n",
       "      <td>...</td>\n",
       "      <td>307499.5796</td>\n",
       "      <td>127158.9192</td>\n",
       "      <td>160546.9613</td>\n",
       "      <td>152849.839</td>\n",
       "      <td>196065.1495</td>\n",
       "      <td>150636.9317</td>\n",
       "      <td>222878.6388</td>\n",
       "      <td>268039.4558</td>\n",
       "      <td>194401.6285</td>\n",
       "      <td>173666.3594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3594 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0          1          2          3          4          5     \\\n",
       "ts_code     000012.SZ  000012.SZ  000012.SZ  000012.SZ  000012.SZ  000012.SZ   \n",
       "trade_date   20241225   20241224   20241223   20241220   20241219   20241218   \n",
       "open              5.4       5.31       5.38       5.41       5.41       5.48   \n",
       "high             5.42       5.41        5.4       5.42       5.42       5.52   \n",
       "low              5.32       5.31        5.3       5.37       5.33       5.42   \n",
       "close            5.35        5.4       5.31       5.37       5.41       5.43   \n",
       "pre_close         5.4       5.31       5.37       5.41       5.43       5.45   \n",
       "change          -0.05       0.09      -0.06      -0.04      -0.02      -0.02   \n",
       "pct_chg       -0.9259     1.6949    -1.1173    -0.7394    -0.3683     -0.367   \n",
       "vol          112381.9  129886.92  166449.73  127785.89  151824.59   138202.3   \n",
       "amount      60072.316  69849.238  88952.487  68906.931  81634.241  75580.771   \n",
       "\n",
       "                  6           7          8           9     ...         3584  \\\n",
       "ts_code      000012.SZ   000012.SZ  000012.SZ   000012.SZ  ...    000012.SZ   \n",
       "trade_date    20241217    20241216   20241213    20241212  ...     20100115   \n",
       "open              5.56        5.61       5.73        5.64  ...        18.53   \n",
       "high              5.58        5.67       5.78        5.76  ...        19.55   \n",
       "low               5.42        5.53       5.64        5.58  ...        18.41   \n",
       "close             5.45        5.56       5.64        5.74  ...        19.32   \n",
       "pre_close         5.56        5.64       5.74        5.64  ...        18.53   \n",
       "change           -0.11       -0.08       -0.1         0.1  ...         0.79   \n",
       "pct_chg        -1.9784     -1.4184    -1.7422       1.773  ...         4.26   \n",
       "vol          213145.79   247121.13  471299.41   434181.81  ...    160030.34   \n",
       "amount      116813.336  138208.259  268798.53  247756.399  ...  307499.5796   \n",
       "\n",
       "                   3585         3586        3587         3588         3589  \\\n",
       "ts_code       000012.SZ    000012.SZ   000012.SZ    000012.SZ    000012.SZ   \n",
       "trade_date     20100114     20100113    20100112     20100111     20100108   \n",
       "open              18.21        18.43       18.16        18.78        18.63   \n",
       "high              18.58        18.69       18.92        18.78        18.69   \n",
       "low               18.09        18.09       18.05         18.0        18.19   \n",
       "close             18.53        18.13       18.85        18.11        18.43   \n",
       "pre_close         18.13        18.85       18.11        18.43        18.63   \n",
       "change              0.4        -0.72        0.74        -0.32         -0.2   \n",
       "pct_chg            2.21        -3.82        4.09        -1.74        -1.07   \n",
       "vol            69393.29     87698.17    82240.44    107746.62     82173.51   \n",
       "amount      127158.9192  160546.9613  152849.839  196065.1495  150636.9317   \n",
       "\n",
       "                   3590         3591         3592         3593  \n",
       "ts_code       000012.SZ    000012.SZ    000012.SZ    000012.SZ  \n",
       "trade_date     20100107     20100106     20100105     20100104  \n",
       "open               19.5        19.04        19.33        19.79  \n",
       "high              19.64        19.68         19.5         19.8  \n",
       "low                18.0        18.91         18.9        19.36  \n",
       "close             18.63        19.67        19.08        19.41  \n",
       "pre_close         19.67        19.08        19.41         19.6  \n",
       "change            -1.04         0.59        -0.33        -0.19  \n",
       "pct_chg           -5.29         3.09         -1.7        -0.97  \n",
       "vol           116935.61    138097.89    101992.39     88680.55  \n",
       "amount      222878.6388  268039.4558  194401.6285  173666.3594  \n",
       "\n",
       "[11 rows x 3594 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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il2BgJB2KU0Sz1zzUNCC/IHWDyVIMBhIRERERERERpYCIOGsG2uX5MvwiyQxsqLOWO3d37suEzMBu5QAAbcLdQNce1nalgjMBVy6D1NUkcXDZh8FAIiIiIiIiIqIU0KfcEnKfMjPiIun826mr7bx8qLH7WvtcMwPTLBhodlT2eBz1DwFA7bhr0OHy7WdJGFT2YjCQiIiIiIiIiCjJpLkZ+O3n0AeYwUCJIDOwrCMAQJ3wN+PrX/7u36W693J58DSbJmxOW9ZcgoH5BdAuu9V5fGDHZYoKg4FERERERERERMlWvTX8/mimCfsy61SP3sa6velG/8Eux6drZmCOMxhoThHu1tN5PJuIxCQn1QMgIiIiIiIiImp3aqqs5f5DgOWLnfvNYGAkgbvGBuNrbh4AQBUUQrv+X8Z6nksWXbrVDDS/R4/H6CAcqEu3gOND1FmkiDAzkIiIiIiIiIgo2Wp8mYG9+0M7dlzw/mhqBlZtMb76pgsDgBqwDVTv/kHTbgFA6uuCtqWUvWYgVNBupXmgTZwG9BlobAjVdIUiwmAgEREREREREVGSSbUvM7CkzBcEC+AL7Ik9g9DtOl6vFQzs2Dn4AJdgIH7+PvKBJoNuywzMsU1iLSrxL6pu5VB9BxgrzAyMCacJExERERERERElm69moCrtYNTKC6DKOkEAK9AXSm2VUUNPKaC0LHi/5hJo9KRZbpiZ/ah5oDweaFfeCbS0QBWXOI8zf07MDIwJg4FEREREREREREkkWyohv/5orJSWuQYD/VN+WwsGNlvNN5RL4E+5ZQZ606ebsKxZYTUE8f0c1LDt3Q82swaZGRgTBgOJiIiIiIiIiJJIv+rv1kqJe2YgyjoYX2u2QkSgVHAtPQDOTryREj38NZNEmpuh33yxtcFturSd+T2mWzfkDJNmeaFERERERERERO1IaZmzTp7JnCLb0gI0NYU+39+JN8oQj54G2YHNAd9XpMFAThOOCYOBRERERERERERJIuaUWFNJByA3L/jA/EKr+UddTegL+ptvRDn5U0+D7LrAKcytBQN9QVP5/VdIxboEDSr7MRhIRERERERERJQk8vHbjnVVWgb06AV14NHO7UoBxaXGSm116Av6pwm3EkgLOi8NgoEICIy6NTuxM7/H5YuhXzc+MUNqBxgMJCIiIiIiIiJKEnnlKeeG0g5QSkE75Vxo978A9OoHddxpxr4i31Th2jCZgWYzkNYCaYHSITMwgGuzE7tosx/JFX+KRERERERERESpUlLmX1QlZfDc+pC1z6wbGG6acFszA9OhZqC0fohDTm5ChtHeMDOQiIiIiIiIiChZAoN25lRgN77MQAk3TdhfMzADMwMD6ye2xq3RCkWNwUAiIiIiIiIiomTZbrRjVYUJcKmIMgPb2EDEmwaZgdHKy3esem++GPoHb6RoMJmLwUAiIiIiIiIiomSx18Xr0Cn8sRHVDPQGXzcS6ZAZGO084YJC5/qaFZBXn4nfcNoJ5lcSERERERERESVLU6PxdcQoaH/5e/hjzSnEicgMTIeagVFS+QVRlxmkYMwMJCIiIiIiIiJKluYmAIC272FQ/QaHP7Y4gszAttYM9KZBZmC0kb38wtaPoVYxGEhERERERERElCyNvszA3LzWj/U3EElEN+F0CAZGGQ0MqBlIbRNVDukPP/yA5557Dhs3bsSgQYNw4YUXok+fPlixYgUeffRRrFu3DgcccABOO+00KKUSNWYiIiIiIiIiosxkThOOILClikuN5Lkw3YSlzdOE0yAYGK0oxiwtLdDvvQ6qR29oZ16awEFlnogzA9etW4dHHnkE48aNw2OPPYauXbvi8ccfR3NzMyZPnoyBAwfirrvuwqpVq/DZZ58lcMhERERERERERBkqimAgiouNr5HUDIykgUinrkDHLsZyWtQMjDIzsGefyI/98w9gyULINzMhLc3RPU6WizgYuHr1apx66qnYY4890LFjRxxyyCFYsmQJfv75Z9TV1eGMM85AeXk5Tj31VHzyySeJHDMRERERERERUWaKKhjoayASYpqw/PYzZNbnxkqkmYHmdGJvGgQDo5wmrIpKoN0T3D1Y3DIG7cHOyopoR5bVIs4hHT16tGN9zZo1KC8vx/LlyzF06FDk5xs3cf/+/bFq1aqw12pubkZzsxWVVUqhsLDQv5xtzO8pG783Sk+85yjZeM9RsvGeo2TjPUfJxPuNko33XJI1GQ1EVH5B6z9zMxhYXwuIQNmy/0QE+gM3+9eVxxPB71AAzQgGKtFT9jt3u+e00y+KaDyqU1cEhjFVSwtUvjPEpf/whbVSuRGqR+82jzfbRDmh3NDS0oIZM2bgyCOPxIYNG9CtWzf/PqUUNE1DTU0NSkpKXM9/44038Oqrr/rXBw4ciMmTJzuuk43Ky8tTPQRqZ3jPUbLxnqNk4z1HycZ7jpKJ9xslG++5xBMRrGo2MgN79O0HT+eu4Y/v2gWrjBNRXlYKrbTMv0+vq8Fq27EFxSXo2rOn63VW+r56NA9UXi5aAHTp2BH5IY5Plu7du2ONb7nXX89wBDvDWRmw3qNTJ3g6dHQe89l7/uXOxUUoTPH3mk7aFAycPn06CgoKcNBBB2H69OnIzc117M/Ly0OTL9Lt5vjjj8dRRx3lXzcjvxUVFWhpaWnLkNKaUgrl5eVYt24dJNpOOURtwHuOko33HCUb7zlKNt5zlEy83yjZeM8lj/7tp/7pq+s3b4FqjKCWXX4B0NiAdUsXQ3W3Alqycb3jsIamJqxduzbspbzeFv/U3I0VG6C1cnyimPfchvXW97B23bqIMxU9tzwI7y2X+NfX3HwpPJfcAFXknpRWuX5dyr7XZMrJyYko0S7qYOAvv/yCjz76CHfeeSdycnJQUlKClSudMdn6+nrk5IS+dG5ublAA0ZTNTzwiktXfH6Uf3nOUbLznKNl4z1Gy8Z6jZOL9RsnGey7x9Kfu8y9Lbl5kNfMKi4HGBkhdjeN4qQnoMOzxRPb708yagd6U/74DHz/i8fTu71z/4zfoc2dB221/a1thEVBfZ1y3qTHl32s6ibiBCACsX78eU6dOxTnnnIM+fYwOLkOGDMEff/zhP2bDhg1obm4OOUWYiIiIiIiIiKi9U2GSqBzMZKrAmZSBHYbNIF84YjvOrelGssUzQNfQ4Fzv0sNarqmG/p9pkAVz4/d4GSziYGBTUxMmTZqEMWPGYMyYMWhoaEBDQwOGDx+Ouro6fP650b3mzTffxPbbbw8twnneRERERERERETZTn/xUWulS/fITzS7BHsDgoG1gZmBEQYXzXhNcwRTlDNJc0C5OluwU977L+STd6Dfd2OSB5WeIp4mPGfOHKxevRqrV6/GzJkz/dsfeughjB8/HlOnTsULL7wAXddxyy23JGKsREREREREREQZR5oaIbaGFto/bor8ZI8vky8gM1ACMwM9kSRlif96+iMTod3/AlRJWSvnJEEbuhprE+6GPulqa0NTo/MA3dZzOHBKdTsXcTBw7NixeOWVV1z3de/eHVOnTsWSJUswbNgwlJWlwY1ERERERERERJQOAoNRBYWRn5vjmyYclBkYGAyMIMQjYmUGApBZX0AdcFSYExIshmnCavBwqBPOgLz+nLEhKBjoPg1aamugitt3abu4zeXt3LkzxowZw0AgEREREREREREAafQFqAKn9BYURX6RnFDThAODgRHUDASctQUjrVuYMGYwMPrMQABQBx8D5OYZK4HBQG+IYOB3n7XpsbIJC/sREREREREREcWZzJsN/ZKToX/8FuSHL507Cwoiv1CIacJBDUQC97sOSpwZczl5kY8jkdoWC4TKyYU64i/GSrhpwnbtPCsQiGKaMBERERERERERRUafcisAQF5+KmifiqTzr8k3/VdaWhwxMwnMDAyRCRek0dZ1N9JswkSJRzPhvHzja4TBQFVcGocHzWzMDCQiIiIiIiIiSlf+moEBwb7AqcehMuHsevUDGmzBwEiyCZOhDQ1E/HKNYKBEWDMQhVHUa8xSDAYSEREREREREaUrM3vP2+zcHjhNOFTwC4B2w/1Qex4E7ezLnbUHmxtDnpMUElvNQAC2mooB33+oTElf8LA9YzCQiIiIiIiIiCiOJJIsvQgpc5rw7G8hLbaAYBTThFX/wdD+/g+oTl0AsY2tuTnkOckRh3nCZuZk4PcSKjjarUfsj5nhGAwkIiIiIiIiIoqnwCm8sTAz3+b/DPnoLWt7Q53zuDCZgc7jbMHAwKm1qRJDYmDIbstuP4+RO0EVsYEIg4FERERERERERPFUVxtyl9rzoOiu5bF6v8q3n1rbgzLhIsxGFFs2XnNTdGOJtzhMEzYzJ9HS+s9D9RnQ5sfJJuwmTEREREREREQUT/UuwcARo6CdcQnQqUt018qxhW46dgYAiEhQIE8i7SZsDwbW14U+LlOYP5/AZihel+BoDsNgAIOBRERERERERERxpc+YHrwxJxeqS/foL2bLDITyTfB0C+JFOk3YHgys3hr9eBIhlm7C/m7LVjBQdN1ZG9HkYRgM4DRhIiIiIiIiIqL4+uWH4G1tzUqzB7A2roNs2gD92nODj9MiDPHYps9KTVXbxhQvZmAyHjUD7ZmBoaZMm52Z2zkGA4mIiIiIiIiI4kRCZei1NfvNftqGtdAnnAPUWZ2E1cnnAF26Qzvx75EO0FpOdWagxKGbsMelgUio30FefuyPlwWYH0lEREREREREFCeOjr92YZqKhKXC53FpBx0DHHRM5NezB+DSpZtwLKmB5jRhe0OVEJmBKtp6jVmKmYFERERERERERHEirz7rvqO2um0XjKWenht7MDDSpiOJFlPNwCgyAzt3a/vjZBEGA4mIiIiIiIiI4qV7TwCAGruvc3ttjcvBEYh3MNCeNefWZCOZ4jlN2F4zMLCzsKlD59gfLwswGEhEREREREREFC++ab1q30Od29MlGOjIDExxMNAvHpmBxjRhqVgHef3fxrbCIuexJaVtf5wswpqBRERERERERETxYjblKClzbh+4Tduu16FTbOMJZM8GDDWdNlnikRnorxnYAhGB/q8bgE0bjG2FxUB9nXUsG4gAYGYgEREREREREVFIUlsN2bAmsmNFgAZf8Kmo2LFPO/OfbXp8tc+hUGP3cd2n3fhA9BfsPcBaDtFoI+liSX40s/9EBxobrEAgEPQ7UPHOssxQDAYSEREREREREYWgXzse+vXnQzZvav1gb4sVYAvIQlOdu7bp8VVuHrRzrwS238W5o2dfqH6Dor6edv41wODhxkraBANjCNLl5fvrBsrcWc59RSXWNGLyYzCQiIiIiIgyhrS0QFqarfU538F700WQlctSOCoiylayZCFQXwsA0O+4DDLnu/AnNDVZy3n50C66HsjNgzrnitgHk5vnXPd42nQZ1bUHtLMvN1ayYJqwUsqfAShP/su5s6gY2sOvQrv4BmiTn4r5sbIFg4FERERERJQRRAT6zRdBv/ZciK9TpP7wRGDtSuiP3pXi0RFRNnJkmlVtMZ5zwmlqNL4qDfDkQO24K7QHX4a2677hz4uACsxw09oWDDTO9YWD0iUzMKZ5wjBqA7pdtbAYStOgRo2F6twttsfIIgwGEhERERFRZmisBzasBbZUApUVzn2+zB0iorhqqI/ueDMYmJfvr0+n2pjBF8RslGGK5bpmIDHVmYHwZQbGWsqvtMx9e3FJjBfOTgwGEhERERFRZjDfZAPBU8vi0JCSiChIY0PQJpn7Q+jjm33ThPPyQh/TVoHBv5iCgb5wkNcbWS3ERIlHN2HA+ffBLkTGYHvHYCAREREREWWGRtubvcA3fpIuU92IKKu4BQM3V7gc6GPLDIy7wMCZJ4bGGLYpxvq0e9p+nbiJLTVQ7bKX+46iopium60YDCQiIiIiosxgf1PeGDB1L17ZJURENuISDISvZqmrhAYDAz700GII6djPXbao7deJlfnUHUs3YQDq0BPcd5R2jOm62YrBQCIiIiIiygyOYGBgZiCDgUSUAE0uwUBvmDp75jTh3NzQx7SVNyAYGEtmoMcWDvKGCW4mXHyeu13rMvYfEjpjsJ1jMJCIiIiIiDKDfWowMwOJKMFE9wKLfgve0dIc+iQzazCw2Uc8BAbt4tFAJF3E2kAEAPILHKvauVfGr3lLlmEwkIiIiIiIMoMtAChBmYGsGUhE8SVP3e++I1wmnbkvJ4asvVDjiWswME3CQf4PcuIQDQyc0p2fgKnaWSJNfvtEREREREThSbMtGycwM1BnZiARxY/oOmTWF+47w9QMFHNfLFN4QwmYnqxiye5zOdf+HCt1NZAFcyF65nzQonY/wLmhpCw1A8kADAYSEREREVFmsL8RDswMjFPdKSIiAEBNVeh94WoGZsg0YRWQGai/9Bj0S06GbFhjrE+6Bvp9N0K+/rjNjxHdgGLPDFQnnwN06W6tJ+J3kCUYDCQiIiIioszgCAayZiARJZCtLqA690pg0DCgR29jQ9hpwr7zEpEZGJiRGMe6f/Lpu4C3BfLea8aGtSuN7T9+FbfHcH/g+D13q+ISqN32i9v1shmDgURERERElBl0KxgoM6ZD6mps+xgMJKI4Mj98yC+ANnYfeK69B2rsPsa2CBqIqATUDFR7HOjckIDHCAp05ubF/zESSG23s7HAKcJhMRhIRERERESZIWBqnv7wRNsag4FEFDtZvRz6e68BWyuNDfYMPzP4FqZmoDVNOAHBwN32gzbhbmtDApqAyLefOh8z0cFAMzMwDtOEAUANGQnthvug3fFoXK6XrRIQRiYiIiIiIkoAPaBO16J51jKnCRNRHOi3XALA9vGCvS6fGeCLpJtwAqYJK6WAwcOtDYmYihwoWZmBcQoGAoDqPyRu18pWzAwkIiIiIqLMEBgMJCKKIwlqTARnIxCPbzmiBiJJCNT1G5yQy4r9w5W8zJomTJFhMJCIiIiIiDJDuDfgRESx2rQ+eJs9M9C3LBHUDExIN2Ef7Yb7oU45F2r3/WO6jtrnUGOhvI9zx7zZ1nJufkyP0Sp/4DF+mYHUOgYDiYiIiIgoM5jBQDPjpu/A1I2FiLKKbFwPrF0VvCPHpWZgc+hgoLz7SpxHFkz1HwztwKOhYqwZqHY/wFgImPasT73NWvHEr2OxO5Z4SAUGA4mIiIiIKDOY04S79TS+uk3pIyKKktRWQ7/2XOiPTQreaavLp8wOtSuXQnQ9/DV//DqeQ0wMTwQ1EJNVnoGJgUnFYCAREREREWUGr+/Nd1Gx8XXDmtSNhYiyx/IloffZm3SUdTS+bt0Mee3ZRI4oOcysv8qNoY8J1zk5HjhNOCUYDCQiIiIiosxgZq8UFqd2HESUXepqQu+zTxPu0Mm/KB++GXSovfGG9vdL4jGyxIqkyUm4rMF44CzhlGAwkIiIiIiIMoNvupoqKknxQIgom0jVltA7m5qs5Q6dQ19DBKirtTYMHhH7wBLNkwbBQJNiZmAyMRhIRERERESZwT9NuMh1t1Rtgf6/VyCbKpI4KCLKeLVhMgObGvyLKkwmnbzwKPR/jrM25ObFY2SJFUlzkERPEzZTAxkLTCoGA4mIiIiIyE9amh1T3dKKWci+0D0YqD99P+TNF6BPuSV5YyKizFdbHXpfQOdg7ZrJ/mWxNTGSL963DvJ4wgYO00YkmYFJqxlIyZQBdycRERERESWD1NVCn3AOMHg4PJfenOrhBDODgTm57vt/+9n4unZlcsZDRBlN5nwP/bVnwwfFmgO6lg8ebtTaa2kBaqqA/G7B5+Tlx3WcCRNBZqB4k9RNmKmBScVgIBERERERAQDkl1lAfS0wb3aqh+LOfFMaSTYLEVEr9IfvbP2g/ELHqlIKKCkDtlQawcAuGRwMTKcGIqwZmFScJkxERERERJnBDAZqEWSzNDW2egwRtV8RZ7x17hq8rbjU+FpbZVwrcCptpgQDI5om3Nz6MZRxGAwkIiIiIqLMYE4T9rT+Nkb/xymQujBNAYiofasM02ho5z2gDjsRKOsI7bQLg/cX+LIFzZqBgc81vfrFZ4yJFkkDkYRPE2bNwFRgMJCIiIiIiAzp/p4sisxAeL0Qs4YgEVGg1ctD7lI5OdBOPAPavc9BuQX2fJl/0ujrNLxqmfP8foPjNsyEiiQzMOHThM1uwpwmnEwMBhIRERERUUaQaGsGJq3wPRFlEhEJrhfoaExkBKZUqACVOQ3YV45Af+Jex261y57xGGbChfz+7BLdTZhSgsFAIiIiIiIKIpKGaYJRTBN2HE9EZLdiSfC28j7+RamvDXu6yi8wFpoajOzAmmrnAYXFsY4wadSJZ4Q/INGZgf6BMDMwmRgMJCIiIiKiYOmYVRfNNGH78UREdhs3BG8rKrKWW6s3amYGNjYCjfXB+wuLgrelKXXoCe47hm1vfE10ZmA6fvDUDjAYSEREREREBvubsnTMqtMZDCSi2Ilb85D6Omu5cmP4C/gzAxutJiJu+zOAY6pwjlWCQY3ew1hIVmYgJRWDgUREREREFCwdA2m6bnyNpAMmACz9PXFjIaLM5ZvWq3bZy9pmT1DLaaUuaV6e8bWpEWhqCtodUS2+dFRc6l9UvQcYC94WiK5D1qxITPkINhBJiQgr7xIRERERUbuSjpmBvgwV5fFAtt8F+PXHsIfLt59ATjkXqihz6ncRURL4Gn+gaw9rm+jQJtwN/eUnof317PDn55oNRJqsa3XuBnXIcVDde8Z/vIlW1hGo2gJ17P8BzU1GINCcNl25EfoDNwML5kIddxrUkX+N84ObAUYGA5OJwUAiIiIiarekuQkqNy/Vw0gf9qyPdJwaZqsZqJ31T8g3M4HiMiAnB/Lkv9zPqakCGAwkIjszgJeXD7X3IZAvP4R2zDiowcPhue7e8OcCVnay7nVcSzvw6MSMN8G0m6cASxcBO+wC5SvDIGtXWQcsmGtse/MFIO7BQEoFBgOJiIiIqF3Sf/gKMu1uqDMugbbXwakeTnqwZwN69dSNIxTbNGFVUgZ1yPEAYExfC3VOk0s9LyJqt6RqM+SL942VvHyo0y6AOuIkKHuWYGvMYKDXCzHLEZhNRTKQKusE7Lirc2Ok5Rhi5U8MZGZgMkVdM7C6uhoXXXQRNmywuu88/fTT+Otf/+r/d8kll8R1kERERERE8SbT7ja+PvdgikeSRuzZgOmcGRj4JrUgTOfO5uB6XkTUfunPP2qt5OVDaZ7oAoGAv4mRLJgDef05Y1tjQ5xGmCZaq5sYN2bNwCQ9HAGIMjOwqqoKkydPRkWFs/PO0qVLMWHCBAwbNgwAoGnsS0JERERElHHsTUPSuGZgUDfhso6hz3Ep7k9E7Y801AF5BcCfi6yNeW0sE2E+B22ptLZtrXQ/NlMlLRhIqRDVb3fKlCnYc8898ccff/i3eb1erFy5EiNHjkRBQea0zyYiIiKi9ku2bk71ENKTIzMwDYOBIboJq5wcoEMnwO332sxpwkTtnVSsg37deGDUWOdzW1un9nqCE6DUUae0cXRpqqi09WPiQdhAJBWiCgaOHz8ePXr0wLPPPuvftnz5cogIrrrqKlRWVmLkyJE477zz0LVr15DXaW5uRnNzs39dKYXCwkL/crYxv6ds/N4oPfGeo2TjPUfJxnuOYiUrljjWW7uX2ss9p3TdKt+k62n1/cq61cDalQCMbsJBY8sPkZjQ1JRW30ck2sv9Rukj2+85/auPjIW5s4CiEv92lVfQpu9ZeXKC6pSqXn2z6uencnMRVDm2sDhu36P/OrbrZdPPL91FFQzs0SN4Hv3q1avRt29fnHnmmSgrK8PTTz+NadOm4brrrgt5nTfeeAOvvvqqf33gwIGYPHkyunXrFs1wMk55eXmqh0DtDO85Sjbec5RsvOeorepXdsZG23rPnj0jOi/b77mtRYWo8i137dwJeRH+XJJh7Y0X+Je7dC9HfsDYth5yLKpeeCzovI7FRShOo+8jGtl+v1H6ydZ7bktJCarNlboa//bu241CbhueH2o7d0HgpOAuvfugIEOfa0JpvO9ZbLj87yjc6yDUf/UxPCUlEf+9jFTnTp1QASAnNzfu16bQYp4Evvfee2Pvvff2r5999tm4+OKLUVdXh6Ii90K+xx9/PI466ij/uhn9raioQEtLGhYqjpFSCuXl5Vi3bh1EQvY5I4ob3nOUbLznKNl4z1Gs9M3Ot3Fr164Ne3x7uee8G60Q6cZ166GKOqRwNE4ta1f5lzdt3gwV8DuTvQ6BVtYZ+iMTHdu3bFiPqlZ+v+mmvdxvlD6y/Z7z1tQEbdPOuAQbc/KBNjw/6NXVQdsqq6qDnpcyXlkX5Dw5A02rlwNffQxvQ0Orfy8jZd5zlZXG3+MWb0vcrt2e5eTkRJRoF/eKkMXFxRARbNmyJWQwMDc3F7m5ua77svGJxyQiWf39UfrhPUfJxnuOko33HLWZrWQNEPlr0Ky/5+rr/IvibbHVckovomnBY/PkQO20G6BpVm1BAFJXk7G/s6y/3yjttJt7rrAIaq+D2/y9ikvTVFEqbZ8zYyVmM5HqrdAX/gI1bPv4Xdv8mUl2x4PSTcxtf5977jl88803/vXFixdDKYUuXbrEemkiIiIiooSQxoZUDyE91dday+nWQETZ3roENBBx0AOqXFVudD+OiLKa/um78D46CbLBJdvM9sFHWyiPS15V914xXTOt5Vhdl/V7r4/vtc0AIOsFJlXMmYEDBgzA9OnT0bFjR+i6jmeeeQb77bcf8vPb2JWHiIiIiCjRGutTPYK0JPY3yHpqg4FSsQ4y53uofQ6Fyi8AioqBWt/UPJesHJMaszfkhy+t61RWJHqoRJRmpLoK8pJRQ1Ty8oHOce5PENBNWJ06HqrQfWZkVgiY2Sm6F0oL86FMWzAYmFQxBwP33XdfrF69Gvfccw8KCgowduxYnHrqqfEYGxERERFRYtQG148iAA22YKA3tbW89TsuA+pqgYp1UOPOAwoKrWBgY2PI89RpFwLbjQZEhzw7FajemqQRE1Ha+HORf1G2VkJ17hrf6wcEwlR57/heP90EBgOffwTYdieoXfZK0YAoVm0KBr7yyiuO9XHjxmHcuHFxGRARERERUcJVrLOWBw5N3TjSTZ09GKiHPi4Z6owpyzJ/jrFun/7bq1/I01RRMdQeB0Dmz4EAMU8HJKLMI/YPARrqjX822sU3xvYAgVlxhcWxXS/d2aYJA4B89RHkm5nwxCMYyDqBKRFzzUAiIiIiokwj9mCgW+2n9iqNMgP9zOnKLUbTF+3GB6BCNCN0KPK9OWcwkKj9abJlDzfUQz55x7+qDjwaatSY2K4fWLc0m6cIA+51WnUdElijtS1YMzAlGAwkIiIiovbH3igjxbXx0koa1Qz0M99smg1NcvNCH2tnZurYf9dE1D7YSwmsXWktb78L1NGnxH79oGBgdmcGqlCBuubm5A6E4obBQCIiIiJqf+zdhNOta26KiIgjM1DS5ediBgPN4KQnwrcwZmZgQz0kXQKbRJQcTe51RbULr4UqLo39+oHThIuyOxgIANh5j+BtzaHrt0bOnCbMzMBkYjCQiIiIiNofe9ZIugS9Uq2pyfmzSJefS2AwMNIOlvkF1nKYhiNElIVCBANVTgQlBiIRkBmoIs1YzmDa+ddA7X2Ic2NTU+wXZiwwJRgMJCIiIqL2p9FWTJ5ZY4aGgNp66fJzMcdhNjSJNBiYY6sFmS71D4koOezZ3z7qjEvid/0QwcZsppQKKtOgv/RYikZDsWIwkIiIiIjaFRHhNGE3gbX1kvBzkS2Vxu8jnKBpwpEFA5XmsQrS83dM1L6YwTpbrTu150Hxu37PPvG7ViYJzKycOysOF2UDkVRg6zQiIiIial+am6zuhUD6ZMClWmDX3QT/XPRP3oH8ZxrU0adCHXNqmAN9HSvN31mEwUD/sS0tzAwkamekpgoAoE4+B1i3CujeK3QTjDZQZZ3idq2M0hSccRk/DAYmEzMDiYiIiKh92bjeuc5AkSEwGJjgn4v8Z5rxdcZ/wh+o687AZKTThAHA48tiaeHvmKhdWb4YAKD6D4b2fxdAO/jYuD+Edt29wODh0G64L+7XTlvxqBEYqLXscEoIZgYSERERUbui33yxcwObSxiCgoF6asYRSPc6p/lGmxkIcJowUXtTtcX42qVHwh5CDRwKz4S7E3b9tNSciGCg7yunCScVMwOJiIiIqN2QlubgjfZmIu2YmG+eTemSMangDOZFlRloBgPT5HshooQTr9eqNZqX/V1+k0mag/+Gtlr3ldISg4FERERE1H64TXFqaXEPErYzsvAX54Z0qqVoH4snircwHt9EKAYDidoPe/ZaDoOBcdXskkkf8/Mrg4mpwGAgEREREbUfLSGmODUmsih6hjBrKRYWG18TPU04xJQw/ZtPIH/MtzYInMFAFcVbmBxfMJA1A4naD3v2Wm5u6OMoaqrf4OCNsT6/CrsJpwJrBhIRERFR+2F/k9hnoNFlsqUZaKgHiktTN650UFdjfC3vDSxblIRsOoXAjBBZ9gfkmQeC80TMwKTHE11HUH9mYBplORJRYpmZgTk5UBrzn+JJHflXIC8Pavsx0O+4zNgYr+dXBgOTiv8ziIiIiKj9sE0f0y6/HSgoMFYamBnoDwaWdjC+JnqasMv7Pgns9Gxu/+lbYyGaeoEAawYStUfm83wupwjHm8ovgHbUKUC/QdZGb4xlNjhLOCUYDCQiIiKi9sPMDOzQCaq0DMjLN9ab2ndHYdF1fzdhVVpmbEx0Np3LdF/l1im4uQkyfZqxLFFOXc5hzUCidsefGcgpwomilLIyr1ti/VvBaGAqMBhIRERERO1H4JtENpgwNNRbdZtKkhUMdEkNbC0bMdraVJwmTNT+mB/6MDMwsfhhS0ZjMJCIiIiI2o/AN4n+aaTtPFhkfzNnZksm+mfi0sFZGurj+xicJkzU/nCacHKYz69sIJKRGAwkIiIiovbDDEDlMjPQwQz8Kc36mSSwZqCsXem+I+7BQON7EXYTJmo/WqwGIpRAcfv7yWBgKjAYSERERETtR2DGiNlpMtHNMtKd+f17PMmZ+rV5k/Phn50CEQEa49zIhdOEidofM/jPmoGJZf582/uHaRmKwUAiIiIiajckaJowg0UArO/f47E69nqjbNYRDc35NkS+ngks/d116nBMOE2YqP2xf7hBiWOWlKitju067B+SEgwGEhEREVH70RwwfSxOwSKZ/zNk7qyYrpFS9mBgMgJomsvbEJH4B2UZ7CVqF2Tp79D/+wyksdH5fEYJowYMAQDI4gVxuiCnCScTJ9ETERERUfvREjBN2Kwp5/WirW9DRNeh338zAEAdeDS0U86NcZApYL551qzMQEnk1GlxSQXR9bgH7ZTHYySdsGYgUVbT77rKWBAdGLCNsawxGJhQ/QYD338OrF0V23Xc/h5QwjEzkIgoTvSXn4T3oTsgunNalf7DV/DecTmkYl2KRkZERH6+acIqqJtwDMEiW507mTkDYmYfZhLdJTNQT+A04WaX6cBNDfGv3ZiM+odElDZkwS+2zEDmPiWS6tELACAb1sR4JTMYyMzAZGIwkIgoTuTjt4G5s4Bli5zbp90NLF8M/cVHUzQyIiLy808TDuwmHEMQqr7Oud6UgcFAM1iWrGnCbrUBmxrj/5icJkzUvtRsZc3AZCkpM77W1cbneowFJhWDgUREcSD29PZQxc9ra5IzGCIiCi2ogUg8MgPrAx6jse3XShWXacIJDaC5/K00an3FORuRDUSI2pfqKueHG5Q4ub4P1WJt/MRZwinBvFkioniwTaWSX34EthkJxTolRERpRXSvlcGQG5gZ2PYglCxZ6NzgNgU23dmn1SUhm06SlRnIacJEWU1/9VnIymXWBm8LsHWLsczX4omVE6dgoBkNZAORpGJmIBFRPNjeZMiHb0A+/yD4GBbHJSJKKf3BOyAfv2Ws+DIDVRwyx+S5B50bMnGasDmtTtOgPJpzWyKst2pMqdF7GgsN9fF/TA+DgUTZTD54HZj/s3PbvNkAbM/vlBhmMLA5Xs+vDAYmE4OBRETxENClUGZ/naKBEBG1D7L0d+hvvQSJJnvN9wYRgEtmYByDUJk8TThZmYHvvGytdO9pbHv1GUhDfYgz2siThCnPRJQS0hjiuXbp78ZXBgMTy58ZGP0HYNLSDO/U27D1348wYSJFGAwkIoqHwDcZq/9MyTCIiNoDEYF+11WQd6ZDvv+8bRcxA17xqCk3cKhzPZMzAz1Jqhlo16uvtfzzd/G9tvl7bmFmIFHWqakKv5/BwMQyP1TzeiHRdp//fR7klx9Q9fLTgBnU5TThpGIwkIgoHrwBtTJqqoNrSBERUcxkzQrod19rbVj2e9sulJ9vfI1HFlxTQHZKcwYGA5PdTdhH7b4/VK9+iXsAThMmyl6tBQNZMzCxzMxAAFi+OKpTpc5qrCirloU5khKFwUAionhweRMp61cHbGAKPBFRrPQHbwcWz/evy2fvRX6yGeQasA3UTnsYy2ZQcO3KqMciTY2Qn78Dtm42NijfS+uMnCbsy+rQbMHAaDM9otG7PwBA7bY/UN4ncY/DacJE2avWGQxUZ1/m3O9hv9SEyrWCgfrEK+E99xh/vcZWbd7kX5SP3473yCgCDAYSEcWD2/Sj3+ZAGhtsGxgMJCKK2cb1bTpNRPwBIe2SG6G6dAMAqBGjjP3RZjVsWAt5+Snoj0y0slM6dTH21dW1aYyJIhvWQP/+c+NnEOoYe2ZgYZGxXLUl7DkxsdUoVHn5QGB2YE6c3sSzmzBR1pLqgGDgoOHOqaacJpxYLsFWfcqtkZ3rltXJacJJxWAgEVE8uLzJkFmfGxkj/mOYlUBEFAtZvMB9eyTPr/bnafsbmM7dja+2KUutjqO+Dvqdl0O+eN+xXQ0ebixUVkR8rWTQrz8f8uS/ID98GfIYeeo+Y0HTgJ59jTfRtdWO7I24sgcfAai9D3bsVmP2dj1Nu/z26B6HNQOJsldNtX9RHXQM0K0cyC+w9jMYmFAqluBdvcuHZgwGJhWDgURE8RDiTYb/zRUA1NpqYyxZCP3ZKZCqLQkeGBFR9pANa9x3+Gr2yfLF8F75d+gfvhl8jD1gaH+DWFxsfK2pjryT7cplQF1t8PaevkYYbQwGikjiMvEAYNE898fVvdaU4A1roXLzgOJSY72u2vWcmHltDUsAoKjEsVvtMAbahLud206/yJ/JGTFfMDCqrtOU9fTnHoQ+/YlUD4Ni5csuU/sdAe3kc4zglP15nDUD01e9y99QSioGA4mI4kDmzmr9INsbKn3S1ZCvZ0K/5ZIEjoqIKMuEipP5goH6848AWysh/30asnaV85hQmYG2IJR+ycnQP34LMn8OpDF03T9xqy/YsQtQWmbs//JDSGBTkVbIvJ+gjz8W+g0XxDUg6LhWqDfG9kCZ5nt7kJtnfG1OUEZdQDBQFRU793tyrExLH7XnQdE/ThKboVBmkE0VkK8+gsycEfX/U0ozZkDJ/vxh/7CHmYGJZ/6tiJK4ZQZSUjEYSEQUB/L2S60f1NQEqauF9/6brG3VW8O+4SQiIpvGEJl75ht6W1BLPnjNeUyIzECVl+84TF5+Cvr9N0G/6u+Qyo3uj+eyXfvbRYDtWjJzhvO6v/8K77XnQub95HpJfcotxsKGNcHdiWOxYK61HOqNsVug1OwSmajOyLpVMxAAUOjMDHQLXKq2vLGPR7doyi667V5gMDCzmc9PtoCUNv4qaz8zAxNOu+OxoOm98ucfrZ/olhmoGJ5KJv60iYhiJK29UcovtI79ZiYwf45zf0tz/AdFRJRhZNkiSGt1++xNmbp0D95u62wo9UbgUP/4LXivOQtYs8LYoWlQWsBL4KHbBj9WfW3oGnubndOAtRvvh9p+F2dgMWCqsP7Co8DG9VbQL5w4BuAcGZKOplY29kCZ2XDD/Jqov1EBNQP907VNeW3LNgnCBiIUyH4vhPo/QZnBJRiIQcOs5YJCUGKpzl3hmfYWtAde9G+TJb+HPF503Sjb4JYZGK/GURQRBgOJiGIkrdScUaPGWCtuLzr5BoWI2jlZshD6xCuh33F56GN0L+T1fwMA1AFHwTPpSSsgaGb32Kb/mgE/efkpoHIj9HuvDzrGpJ16nvtj+hqWBE7bla1brJV+g4G+g4xlezDQVktW5v8MrAuYthxOcxwDcPa/MaGuaz/G7CTsnyacqGCgLwBpZu4EZgZ27RGXh1GcJkyBmmzB9gYGAzOa+fxk+yAIebYGIvH6UIFapYpLrQSIEH1ARAT65Gug33Kxo/mLH4OBScVgIBFRjOSLD8IfUFJmvaly+yPHDodE1M7Jbz8bCxXrINVbg/cvWQj9vOOtDbW+NxFm1ofvgxbVvad1zo9fQebNDn4wt6mmJaXuA1u5FLJ8MfQr/gb9s/es7Q1GRoM65wpoV0+yOirau1i2tEB0Hfr3n0P/OGDK8K8u47K/aW0Onrqof/IO5Ncf3ccZjj3rL1RArMU6Rvu/C3yDNAKgMvur6B8zonGZmYG+v4uBmYGdu8XncThNmALZMm/lyw+gv/io0USHMo64ZQY6ugkzuJRMasxeAAD57jP3A7ZuBpb+DqxbDWwJ7lSv+PtKKgYDiYhiIMsXt35Qcan1IqXOJSWe2QpE1N7ZpnLZa+pJbTXklx+gPzvVefyoXY2vZtF4MzgYUP9Ln3Jr8GO51WktLnMf16YNRrZi9VboLzwCvbYGsmWTf3qT6tAJKt+WDWh7IyNNDcC82ZAn/wUEBPH0qbdC1ludkWX5Eme2UpNzmrAsWQj5zzToU29zH2c4tr8xEurvjbk9Lx+qd39j2ff3Tb79NPrHjGhcAd2EbSU1UFLWtvqAbvzTnfm3lnxszxMycwbks/eAn79P4YCozfzBQCszULGBSMrIUt/04GWL3Pd/MzN4oz1RgsHApOJPm4goBvqzD7rvUMqfVYESezDQJSWeb1CIqL2z1wpcuhDYfX8AgP7IXcCieY5D1bjzoHbZ01jxBfH0x+8GPn4bWLKw9ccSPWiTys2FOvEMYFMFUFQMtdv+0O+eANRUOY6ruOkf8C78xdpgTqn1X9s2nXhTBWTVnyGHod9wPjBoGDzX3gPZsMa5M2Bqrpj1DgFISwtUNFOp7Blxof7eeAOaeQQQESv7MQ5E91o/K99j2q+vdt4jbo/FbsIUxKUOpsz/GdhpVyg2nIgrEQH+XAz06uf84CRefMFAFaKjrerYOf6PSSGpPgMcf6+CuPxNzCnvjZZVy40VBm+TipmBRESxsE8r6dzVv6gOOMraXlxqfWJZ63vDa/9jxzcoRNTe2YKB8tl70L+eCf3NF4ICgdo9z0Lb/0grcOSxvZSNJBAYhnbYidD+73xox58O1bOPsyOlT5M9EAgABQHBQPv0tI3rW69XtfR3Y5pbYEfTwGnCtdWOZRGBLJgLaQjRXdnO/jcmZGagGQwM8dagakvrjxONEJ2d/UJ1jW4LThOmQE3BDXrkiw8gH76Z/LFkOfn2U+gTr4D+oJHVLD99C++tl0JWLYvPA/hrBjqfa9W5V0Id/hdgxI7xeRyKiDrhb/5lt6n3bk3CPF1s9WFZMzCpGAwkIopFaQdr2SwgDziKyKuCIv+LFPEFA9Vu+wOdfMFDBgOJqL0LKCQuz06B/O+V4OPKOjpWVagmE/ZOw+ax+xwa1ZDUiFFQ+x8R/qDATpV9B0KN3ddYrq12Lw0RqKbaJRgYkBloD0JWbwV+/hb6fTdCv/Ny6O+9akxdDiWizMCA+n0B5J2XneuL5hl11upqQz9uOKGCgeZU4RGjrG2B2ZfRYmYgBRCXmpwAIG+9lOSRZD/58kNj4fdfAQD6o3cBq5ZBf/35+DzAn38YXwOCgdrYfaCd8Le4ZjRTBOzvi1yC7nD5m6GV2c7hNOGkYjCQiCgGqs8AY6FLd8enWeqgY6yDOna2XqSY2R15eaxjRETkI1s3t35QQaG/Q7BJ7Xs41F4HBx2qXX0XtBsf8D/3arc/AnXahVCnnAvtqokRj0udeCa0m6YAI3cK3tmt3GgQZT9eKaizLwPMcW7aYI3pwuugzOYcNjJ/DuSlx50bA4MVm61gn/wxH/r3Xxgr61ZDXv839CfuDf1NODIDQ2THBdbvCxzjZ+9CfH+rRNeh33Md5LP3ID99E/pxwwkRDNRuexja+ddA+aaJA4DabrSx0NaMEY8vM59/a8lUHyLz1GX6MMXIFswP7MoeK5n7g7Vi7yZMqWMPyja5dOqudwYD1QFHQbMHEDlNOKkYeiUiioXvhaPa40DI6uX+zaqsI7Rr74GsWw3Vf7BtmrAZDMy3Pv3iGxQiau+2VLpv9+RAu+0hyOcfQO1xQNBu1a0c6oxLIKeMh37xSdaOjp2hOneDNukJQHmgSo2gnTrw6KiGpfLzjWy/8t5GTTEA6NwV2sQnAF13bXKhNA0o7QhsrYRsWGtsO+xEqJ12gwIgex0MmfU55JkpAIwsyEBSVwtHPottOrC89BgwdFvnCYt+M/Zt2QQoDapDJ2tfJN2E9eCagdqEu6FPuto6prEByClxjAWRBHHd2Mdhq9GmOnd1lNwAYARQO3R2BAij4s8M5DRh8on3tHcKSRUUwgwB6jddaG2PMeNXfvoW+mvPWRt69YvpehQfSinjPU5To3uzrsDMwNw8eOyZgZwmnFTMDCQiioX5KXJuLtTg4Y5datAwaOabV/OTso3rja9lHa0/eF5+Ek1E7ZdUrAN8DTTsWdXqqFOg3fdvqO69oJ10ptXl1oXKzwc6djFW+gz0NwFQZZ38gcBYqB3GWCuVG6E8HqhwmSi9fW9MF883vtoK56ucHGh7HGjUkw1l/VrnemNAhoUv+GcnTY3QrzoT+pVnOGs1RVQz0JwmbAvMBfxN89d1bLBNfdaDm7FEpMWalhyY7RlIFZdAO/lsqH6Dwh4Xkr9mID94Ix8GA5PHnj29brV/UWZ9AalY16ZsQamtMaYbm383TjkXqqgk5qFSnJi1c92mCQfUudUOOS4gM5DBwGRiMJCIKBb2NzQHHAV1ynhotz0cfFxgl7NOXVnUnIjaPWlsgH7deP+6Gm3rItu5a1Rv8LTTLgS23wXaeVe3fnCU1La2acLlfVo/fqfdnBvyXLpoupWy8n9wtM65vbVGIYXFQGWFtV61FfoT90L/+mNn9nmoTHTzg60wnVT12y413rivWBr5uEIxA3PJyAJhzUAKINVbUj2E9iPM/3H9uvGQt9tQpzGgwZDq0i36a1DimH/vAqYJi0hQfVzVoRNrBqYQg4FERDEQe2ZgTg60A4+C6tk3+MDALmcdOrNmIBGRvbzCcacZQS1zvSS6jD41agw8/7gJqrx33IZn57ntERTufTA8F0xofSy77efcUOz2vbhEA4uN4KfZmENWLIFUbbYCWf0Guz9gfS1QXeVflU/eMTJvnp0aME3Y/cMnfzH/NSsCxmPLXmyoh8yYDv3hO63H+fANfy3BqLSEb1gSVznMDKQAgQ17TLE2q6FgzeFnv8g7L0PcppOGE/g81reNWcOUGGZmYOCHRfNmux6udehsrTAYmFQMBhIRtZH8OhuY7Sue3sofr6DpZPlWzcA2vZEiIsoGtjfl6vC/GFN9c3zPlwOHpmhQ7lSvvug64a6w05X9xxY4gwpqp10jexDzTdG82ZAlC6Hffhn0my629ncOnQGj320LUpolKQBnECxUg4Tli103a9c5G5PIjP8EHSOfvx9yTCGZ5TGSkhnID94oQKh7oaEe0tap7+QugqYsMue76K7ZHDD9NMzzIqWA70Mtfdo9ENvvSp96m+vheQO3sVYCf7eUUAwGEhG1kT71Vmslp5UuZoHThD05rBlIRGS+8O8/BErTjPpw194DbeI0qI6dw5+bQdymO6sjbA1PSjsAvftDO+Iv/k36U/cZC2bjKcDxM1EHHQPtun+5Pp7Ygqziq6sFIHQQJMTfMNW9p/vx9seaPi36D7VakjhN2JyyputWNj+1b6HuA5Hg+pwUG5fgjvrL350bov2ZB1xTKbeaC5Qy5t+7mirIR28BcOkkXVTivw889qZRbCCSVPxpExHFQ2t/vAIzAz05zFYgIjILjNs+MGlzo4gMow48GmrICKDPACjf9y/L/rAOqAioG7jdaKhRYyGfvWusFxYBfQe6X9w+DXLlMmt5ayWkuiq4qcp2OwNzvoc66cy2fTML5wLbjY78+GROEzanrAG+jsitfHhH2c+8/3bcFZjzvXNfYz2nC8eRBEwT1ibcDfQdCHn7P/7nKXn+YWCfQyO/qD0YOHLHOIyS4sr++zGb9dTXOQ7R7n/B0TzKc8P90D9/D2rfw5IwQDIxM5CIqA0CP+FSrWYGBhSP93isN0Eb1kBqqoLPISLKcv4Mtry88AdmooJC4+uQEa67laZBDRzqDwQCALTQGS7aaRc6g3/5hVChPogKeOPlsHRh8DazBpdrbcPWyYa1rR/keDwzMzDxgTmVk2M9TlsbnlB28WUGavsfCe2+56FOtZoYtVbjjqIUmBnYvRdUXj60Ox93bJbqKF4H27rUavYMa0oLqpetlIbvuV5+/tZ5TEAXeTVgCLS/XQxlr1NLCcdgIBFRW9TXOtdbeyMbmBmYk+N/EyfvvQb9xguCU+iJiLJdsxkMdOm2m+G0KydC7bovtHOuiPyk3gOCNqm9D4H2wEtGx8yyjkCR0WRFmZl4gZ2LAWDr5uBtvvNk0W/wXn0W9A9et/aZ0yY9obsJO8Z0ynjnhor17geGkszMQMAKzDZwCijBNk09F6q0A7QDjrKmNnK2RtyISHCGc4kR7FEdO0PtdbB/s/6gez051+uapRMGbAM1bPuYx0nxpfa1ZXmaH0yt+jMlY6HwGAwkonZLf/3f8N57PfQZ0yG6e4fFkLZUOtdby6YIVzMQAGqqgTUroxsDEVGm82V4qMDnyCyg+g+Gds4VUF26R35OTg7U/kdYG4Zu68uWMAIVSimjpuL1/4Lq3Q8AoB10TPCFtlYGbVKjjCYm8uEbwOaNkFeftXb6MgNdMw37DHCuD98B2oFHQbv3Of8m2Rjwhr81yawZCNi6W4bJmKT2o8WlgY35oS3rSsaNzPgPsG4VAEAdOw7aHY856vs5agcuWxT5dZ9+wFj484+wx1FqqPI+UKdfCAAQMxu7zpZEMWRkCkZFbhgMJKJ2SWqrIe+9Cvz+K+TtlyAfz4juAoHBwJJW0tpdMgMDMyL0Wy6GPv2J6MZBRJTJzClk2ThNuK0Ki/2L2vF/C9qtyvtADbB1X7RPqzK7arp1RO0SpuOmN3SmnnbVXVAHHWtt8AVQVIdO0C692dgWmP3TCjGDlckKBvoyA2XhLyzLQY7MQL8cBgPjSRrqITOm+9fV8FFQPXo5jmnzlFD+jtJfga/upi8YaD7vqmPHQbvi9lSNigIwGEhE7VOdc5qvLFkAWTwfElhIOgTZvNG5oTi4U6RDuJqB9uvOnAHZvMla172QTRsiGhMRUcYxu0gGPke2Z51snRX7RtBMxf73p1t5ZMcFCpOpp4qKoXbZ09pgD6CY3Y3NIvERkCULjYYBQPKmCXczuiLLmy9Av/va5DwmpS+3zEDzXmTNwLiQmQEfsptT9cOdE+nP3mzwsv0uUY6KkkWZv6PqrcZXMxjYe0DrddYpadhNmIjap8Caf9VboU+eAADQJk6DCveGCgDWrXauF7TSec6tm3CojIg1K6B/+QGQmwd5979AQz3UqeONmjZERFlE1hpTyMJmrbUzavf9IXNnQfUbBJUfQZC0yAryqdIOCFl9Nq8g1B5bZmCImoGlHazHsAfw8n1v8Bsjb8yhv/yktZKkzEC1y56QOd8ZK2tZkiOdSXMT8MsPwIgdoYqKWz+hLdwyAzlNOL4qAz40DxUMzMuzGoLUVgEdu7R+7Q6dgfo6aIeeENsYKXH6DwaUAtasgFRt9gcDUdK2JlWUGFFnBlZXV+Oiiy7Chg1WpsqKFStw7bXX4swzz8Tzzz/PIvhElP5qa5zrf8y3llvJxJN5syHvv2ZtyMkJ6ooVJKhmoCfkmyB92j2QGdMhr//bSq//z7Tw1yciyjCi68AfvwEA1KBhKR5N+lD5BfBcejO040+P7Hh785XOXUMf6DZ1GL4unmZxd0+IjA3b1GXY/96Zb/CbmiKvvbvR1mwkSRkiqs/A1g+itCCvPQf9scmQFx9L3IOYXcztr8M4TTi+mgKa9YQIBmqX32GtrFhqBINbY9ahiyDbkFJDlXUCOvkCu5sqGAxMU1EFA6uqqjBp0iRUVFT4tzU3N2Py5MkYOHAg7rrrLqxatQqfffZZvMdJRBRfAdOE7SSwHmAAfcqt1srQbaHd9WTog32UPTNQaVCa+zRhY2w1rpslxBs5IqKMtGCuMYWosAgYNDzVo8lo6i9nQu19CNQOY0Mf5A0O1slP30B/8RFrQ6jMwEIr+93xZt32Zlx//J7IEgLE9rcsWdOEe/QMGEKUTcMoaczppTLr84RcX//0XesedNQM9N2LDAbGhZhdZAdsA3XqeKgQQSA1eDhQ3hsAoD94O/TbL2v99a7ZCIjBwPRWY3R91p970HrfxWBgWokqGDhlyhTsueeejm0///wz6urqcMYZZ6C8vBynnnoqPvnkk7gOkogo3mTZ76H3PXUfxPzUuBXakSdDmTWTwrHXwzJfcEb7Jqhma3THExGlKfntZ+gPGM0n1Og93bvYUsS0Q4+H9reLw745VtvuGLRNf3QSMPsba0OI34Pj99NkCwbm5FqZgj99A/3ac1sPCCrr7Ueyfu+BNarkP2zW1d5IYyP06U9AXrJlHNqm2JuBwYjr1rUjsmoZ9A/fdA3ShQzc+YKB6pDjWy9zY28ksnZlyA/F/Y9n1potZDAwrZnvpVYvN74qBRQnaOo/tUlUf4HHjx+PHj164Nlnn/VvW758OYYOHYp8X02T/v37Y9WqVWGv09zcjGbbE61SCoW+/8z2duPZwvyesvF7o/TEey6YNDZAvv0UaocxQKcukA/eAAConXaH/Pxt8AlzvoPadb+gzfqHbzrWVWlZRD9nsXfK9HiglILyePy1ndTOuwMtLZBffgh5DbVuNZCbn7gaOjHgPUfJxnsu9fSZM4CCImh7HhjVeSIC/d8PWhs6dcmI32NG3HO22otqp90hf8zzZ2doPftC3fk4AMB7/XmupytNa/37a2ly/Cx0zWNNQd60ATL9CSNrfpe93M+3Xz8nJ2k/T89dT8B77bkAAPnsXajTLkjK47ZVRtxvCRbP711/Z3pQUwvNNmtD5eZCAChvS7v9mYe657y3Xmps79wVaszeAAB9zvfQH7oD8OTAM+lJqE4Btf582XuqqKjVn2dgrVNVXwdlq1Pq0NwI+D5wUAWtX5tSRzvuNOhvvmBtKCiCFpAIwee51IoqGNijR4+gbfX19ejWzfbCQylomoaamhqUlLh3LXvjjTfw6quv+tcHDhyIyZMnO66TjcrLW2lIQBRnvOcsFTf/Aw0/foOiA49E0V4Hwyxr3PmwY7HJJRhY2liPsp7OaUVNSxZi/StPObZ1HzQEOa01GwHQuKUCZiVCLS8PPXv2xNbiIvgqaKDXdZNR9fLTqHYJBmqdu0Kv3AjvPdcBAPr+78dWHy9VeM9RsvGeSw3v5k1Y46tl2m3UaORtMyLic/XqKqy2FZfvccSJyA14vk1naX3P9eyJpqkvQisqRk7PPlhz5tHw+oKBPXv2BHr2hF5Xg9UhTu9SWor8EL+LDTuOReOcWehy0t9RZDtmZcC0SvnkHcgn76D8pYPh6dAx6DprcnNhTtItKu2Azsn63ffsCXvrkB4dSqEVhemwnCbS+n5LAPvvqLy8PG5Bgo1bNsLe5iZv2x3Rw3bvbSwpRT2ADsVFKMmg56NEsN9zzav+xDrfcpmmUNqzJ0QEqx7y1frztqBkwc8oPeE0x+9qTVMjvAC69ukX8jnFVNmtO+zFe/Le+y+6TrjL9VjvpgqsAQBNQ8/+AxhESmNNBx+F9bZgoKeoyPg75KK9Pc+li5hz8zVNQ25Al8y8vDw0NYUu/nn88cfjqKOsdGHzP3FFRQVazO5OWUQphfLycqxbt47NVSgpeM8Fa/nRmAZVN/N/qJv5P//2LYNGOo5T+x0O+ew9VG1Yj9q1ax379GVLnMcefSoqWgQIOM6NVFX5l3WlYe3atfBu2eLftq5yM8TleVPtfgD0FUsAWG+c10bweMnGe46Sjfdcasn6Nf7l9f88HTlPzghztJM+Z5Z/2fPQy9iYWxDR82iqZcw9V+SrybR2LbyaVQPQ/NshLrUDTZuavVAhfhdy1mXwVG3B1vI+2BrB72vdZx9AG7tP0Havbv3s6pqa0Jii3/26Rb9D9eiVkseORMbcb/GmlD/za+2ypVC2epWx8FY5S6207H2Y4/WUt8X4f7F140ZUZ8DzUSK43XP6FzP9+6tqa1Gzdi2kpspx3tanp2DrS9OgXXgdtJE7AgC8VVsAAJvqGkI+p5i84gzo1X/5EdYcPQ7KpSGSrFhqLOQXYN26dUH7KX1InbOJjNeTE/Qept0+zyVYTk5ORIl2MQcDS0pKsHLlSse2+vp65ISpAZKbmxsUQDRl800gIln9/VH64T3XisEuBes7dDK+1lYH/ewCG4tox5wa8c9XcmzThHPzICJQA4ca0yL6DACUggTUNFJ7HgR1yrmQKbc4tuveFqMBSRriPUfJxnsuNaTR+SI/mucl/aHbrZX8woz7/WXUPWfrNOwfsxamZHj3nqG/t8JioLA44u9dn3aPf0qhg/19vycnuT/L4TsAC38B4LuHM+D3mFH3W4xE9zp+J1JTFbcmEbJ1s3NDbq7z52rWDGxpbjc/71D0ZuNnoHJyIOus8l/S2GDcj25N+Brqob/zMtSIUUbdbd8x0qFT6//PXD6g8D5wMzy3PhS83cxIrK9r97+ndCeBgfy8gpC/s/b0PJdOomog4mbIkCH4448//OsbNmxAc3NzyCnCRETJJpsq3Hf46pFotz0C9fd/QHv0NX+Xq8BPPaWhHvLMlLYPIs8ZDAQA7DAG2nX3QrvuX87tPtrf/wFVUGi8AbPLwgxqIsowjfXO9TAd2imF8vLct3dPTkZcYNdeWb4YsGdoacmd4qedP8FaCQhoUxrYuMGxKr//GvGpsngB5LefQx8Q2Bgu4DUXzESVdv4aS6+tgfeqM6FPvRUAIBvXWzvN/zMN9S5nAlg0z/i6wAi4Q9McnchDP6hLtvKaFc7O5fB1KK4M8Zqe0k/g/7H8fPfjKGViDgaOGDECdXV1+Pxzo/37m2++ie233x5auE8diYiSRLZsgj7hbNd9ypcFqHr2gbbnQUa3wWLf9KqAYCDMaQnmuUefGt1A7IWQffWVlKZBDRwKZb4AtWVMa1dNtB4rL+CPZzt/oUpEqScz33FuqA3d/ZFSqCDEG/FkvU73TRUEAFm5DPodl/v/Bhobk5sJoopLgN79jZUmBgPTjXzifF6RZ6dCtmxq/byWFuiTr4H+wM2Q6oAPczeuh8z9ofVgoDmrraV9dxNuXPALUL0FWDAXUlnhfG43y9n4OgW7fdgg836C/Oqrf63rkdX06xxiOuPmjc71P35r/VqUNpSmAfaGIYHvZyjlYp4m7PF4MH78eEydOhUvvPACdF3HLbfcEoehERHFTt7+T8h9yqWWkSopNabu+gqu+6+zwaqPpU16CqpLdA2PHAG9UG+a7VOJu9oaNgW+2GrnL1SJKPXkx6+cG6q2AJHWX+vRG1i/GurMS+M+LnLSTjkX+j3LoA45LmBHcDBQu/Tm+A9gcyXQ0egyKnNnBe9Pxd+z/AIAgKxbbQSIRu3KJgTpwuvyYWfFev89FJI9e62mCij1zfLQvdCvP8/qdm0X+NrKLNXS3L5fY1Xec4N/Wb762JkFbgZUfZ2C0bMfsHyx43z97Zeg+g4yVnbePaLHVAceA6xfA3g8kC8/tHZs3QJ07wVpaoT89C2w0aoRqF1yY8TfE6VQTq71/5rBwLTTpmDgK6+84lgfO3Yspk6diiVLlmDYsGEoKyuLy+CIiKIhdbWQ91+D2nFXqEHDjG2bbZ8o7zAG8HXr1a68E2rodsEXKTELr6+E6LrxqRYA+OoFqr0OjjoQGKTOPRioyjrAnyNRXGrtCMoMbN8vVInISepqoD9wC9ROu0M7/MSUjEG/ewK0m6dAZn0BddQpwRnNdr4pZqrPwCSNrv1SPXpBu+eZ4GCXSzBQbTc6jg+sAaI7/t7JWy8GHxem4WDCmMHA/0yDAFDjr4Yas1fyx0FBJKDJBwDHFFL99ecgixdAu+w2KHtmn62unT9QBQANDe6BQMAlM9CcJty+X2Pp9pkxG9c5pgTLx29BjjnVmK4LuE8BLiiE+DL6lFttbhcqPx/qzEshv/3sDAb6pgnLG89DPn7bOn7/I6F2GBPhd0QplZMD+GLIYV8XUErEnBlo6ty5Mzp37hyvyxERRU1/Zgow5zvIonnwTLjb2GgG8U46C9i43gq2dXNvbe8PBgKQLz+E2vcwyIY11puY0gR+2DFiR6jTL4Lq0h3K92YFAJDLacJEFJp88QGwbBFk2SLIrvu6dmBMBv1WI9NP1q6CdtzpUL37OfaL1wv8+iOw1deMKU5dQik816y3OE4T1s6fAP3fD0LtfSjkg9eNjWUdjd+zPTDjJnDqZjIEvCGV7z+DdO8J9BvEDMEUkpZmYL5LzT9fnTrxeiHvvWZs+3U2sPPuEF2HvPYcZP4c6zprV0ENHGqsNIcJNgc+/5jBwLpaSEuzUTqmnZNvPwU8zsZQ8un//NOEVacuCJrov2CutZwfZfOXwAakvsCsfPmRc3uvvtFdl1LH/v8oTs2AKH5Y2I+IssciX6HpJQsB+AqXVxgt7NX2o4GevhcPPfuGfrPc0fahhq82icyxTW3yxO0zlCDK44G2z6FQ2+7k3MHMQCIKQUQcGVf6NWdBKtaFOSNO+gwAAGj/cJlaOud76LdcDGlpcXQHlHdehv7wncaKUq1P/aPEiePfMjV6D2gPvOTMrisz6uS6dh21CexKnQwqMEAxdxb0Oy6DPPNA0sfSHoXsJvr9566NKaTRFzDesNbaZgb55s2GfPgGsGqZte+ZB4wPHoDwweayTs51X91m+fYT6JMntMvOpqLrwR8UBHb6ra2GzPvJWN525/AXtH+wHYnAAGxLC2T5kqCGVWrIiOiuS6ljD/Da66dTWmAwkIiyR571okN0L/THJhufKBcWAT16Qe19MLRLb4Z23T0hL6GUgjrtQuMa5psUe42/VHQfDKxrU99KpgURZTT9u8+gf/EBxOzMGM7CX4KyhfWXn0zQyGzMulr5BdAuv931EP2CE6DfdKG/AYDM+c7aWdrBap5ESaf5/s7Fi1LK+cFVWUfjq/3vldsbwVQEXPq5T0+Xbz+FBNSLExFrSiTFREQgFeugX/E36P+ZFrz/2anuJ9YbAWWZ+7117JP/gmzeBNnk3llWZvjqRYeZhq4Cg14Nttd3f/7haH4TT7JpA/QP30jP+6q+LvS0ah9ZvdwIvioFNXJHqP+7AACgxu4bdKyKMRgoTQ3Q77gs+LgkdUOnOLD/nS9hKbl0w2AgEWUP2xsR+XgG8LPvjefgEVCaByonF2q70VChuiua8n3XMQN/VZv9u9T+R7Z9fGbtlE5RTuELyAzUJ10N/bvP2j4OIkpbsnYV5Kn7IM8/DP2e64w3XuG4vaFMRmdfM0M5Nw9qxCh4nngbaszewcetWw358iMju6fCVuS/W3nix0ghqX6DoE1+CjAzbOKRpWl7I698gT/579OQP+YbG11KXGgnnRX740bJtV6wjzw7xVpubIQ+/ljo/zjF8TdXAjOlqFX6O9OhTzgH+qSrgeqtkE/egf7Ze9DfesmYxRFo5I7+RXn+YePrZ+85r/mfx4FaZ+dg/zn/89W3bw7ODFTnXAHtjseCT6oJqFe4ZkXobygG+uQJkP8+A3n7pYRcPxb6+6+1ftBvvqnc/QZDlXaAtt/h0O59DurIk4KPjToY6MxYlqfuDz6mYxfWnssk9ix0BgPTDoOBRJQ9bF3oxPaCRvmms0VKmRmGTY2Qyo2Qr4xaJeqMS6BieAOrnXcN1AFHQbv8tuhOdHnRI0/dB+/9Cej8SESpVbHWseqfjhWKW+23xfPhvf8miK9kQkKY0/RybS/0dwrROXLDGmCFbapXv0HQTr84cWOjiKjO3eC5ZjK0idOg3fZw7Bfs0g3oOxAYOBSwNYfRn7rPWAgocaFNnQ7Vs0/sjxsts9OpC5n1hT8AL19bdcrE9z3o33wC/fzj4T33mMT+/8oy8tZLQGWFI9tOXnwU8s50yLefGRsKi42vO+8O7Zwrnef/+iOwaYPzor/8aFw31GOKuGYGqrH7QLl0PpcaZ2BR1qwM/Q1FQDashdi7HJt8zTVk4S8xXT8R5L1X/cvqMKsZlTrpzKBj1Vjrwx/VoRNQUhp0TNQ14gLqE7rhFOEMY3uNojp0CnMgpQKDgUSUPey1iaqtT3jViB2iu475SWZjA+R/L1vXifEFiOrUBdqp46HKo3zzE+qT1fk/Q//u05jGRETpxZxS61+f/XX4411qbAEA5s+BzJwRr2EFM6dT5lhlDNROu0H97WJoV010HCp//gExM6u22xmeGx8Iai5CqaO6lUPFoZmL0jzQbrgf2oS7gzoTi0hwvdtos4biROXkQJs4Ddh+F6jdDwjar99yiVE7zSXD1l5XULdlEVIMKtZCtm72TwfWDjkeqrTM8TyiT7V9iGrWdva20kytrta1gUioJjHaHgc6N6z+s9WhhyKbNkC/4XzoN5wPsc0ucdQhTLMsKe+jkxzr6vATjRkto8ZCHXA01MlnO/f3CHgtW+JSBiDaGnGRdBfv1iO6a1JqrbTqeXJGQPphMJCIsoLoumvXQnX25VAjd3I5IwxzmnB9ndGlEwD6DIw+iBcnqiz0iyl5/fkkjoSIEi4wk2TZInjPPQbeq86EPvMd47nOzixnENh4CIBs3hS0zY1s2gBZvCC6cfozA23BwJwcaHsfAjV0O2jX3WtkiQHGVOEv3jeO6co3A9lMaZpRi83epMvjMeqQBdQHDKrZlkSqWzk8/7gJ6pRz3Q+orQkKNgXV8AwViCcHfwOQUPv/9wr0CedYG3yBaTV0O+d95KNddVfwRTp0gnbNJOdMiqrN0XWr3mEMtJunQp1t1KiTP/+I/NxAa1YY97vXC6xa7txu+uM39ynSKSC6F/jpG/+6dsYlUEUl8Ey4G56LbzCe2w861nlSX2ftTaVpjr8HAKIPePbq23rASDF8kbE6d0v1CCgA/zcRUXZobHAtRK7ttl/01zKnCdumpGjHjmvjwOLALMQe7T4iyjwbfc87A4c6t2/ZBJk+zf+GTWproD//MOSFR4z9OblQx5/uPCfMBwkmWbPCqOU1+RrI2lURDdGR5RWiCYgaOBSem6YEBymLSyJ6DMpsjkzDvILgrMA0oYqKoXbf36hDZq9fWLM1qIGEzJ3lnAoZ2I2WXMmPX7V+kP3+MKcLA4BLjWfVvScQ8CGvOvlcqCEjjTqY5lTELZWQwKnFYSiloPoMgOq/jbFhY+TnBhLbvaO/8pRRC1b3Qr/lEusgrxfy5UfBJ6dCQMBW2/sQ9+PKextf+w+BcgnUomtA1l6U04RVTi602x6B2uvgcEdFdU1KEz16Q0UwDZySi8FAIsoO9inCJpdMmYi4TV0qSuEbWHvAb+SO0G5/BGr/I4z1epfvm4gylhmQ0w49wX3/iqXG17dfsjKXAeC3n6AOPi7g4NY7teqPTbYOjzQTxp692NpUz8D9RcXux1H28XX8VAO3CQ4GBga7U0g76zJodz8N7ZDj/H9vZd5PkFV/Oo6TjRuAmmprw+rlwZm6FERmfQ7AWYNOu/hGaBddH3Ss2vsQaxow4M8S9J934XXGcV27Wxu33Qlq9B7G9pIyoGdfAIB+342QtnRWN5+jGura/vutsjUjWb3c6Kr+xgtBh8n3n7Xt+vHWaGW5Fu0TIhAIQLt6MtQJZ4Ssfa2deyVgTsPu2TfklOxwVE5OyA+ZKPNoF1wLDNse2hV3pHoo5CKn9UOIiNKb1FRBn+CrZZJfCO2BFyDfzIQatWvbLpjv0qUsRbWNADimWagu3Y3pyvseDvn0XfcgKBFlJGluAtb4ppSFCpb4notk+WLHZnXKeKjAN1Au3VuDH9QWMPQV0BcRoLEB+uN3A94WaKddCOTlQfk6zsr8OcbxQ7eFau25MXAMDQ2tj4mygtrnEMirzxqBQPM+UAraNZODM4hSzB+06N4LqNoCeeUpa2fHLsCWTY5plAAAbwv0846DNu2tNgU92o31awAAatRYq7lbcQmQFzCldPQe0P4W0FjIHgwcMhJqp92M5Q5WwFA7/WLHlHNV1gmtfwwShvmYvufBwIBkRKq3BG0St069OWkS9LI9L3e+/Fas27jR9TBVWmbUEgxB9R0Iz7S3IJUVzgzPaAX8XNTRp0BmTDdWevVt+3Up6dTOu8Ozc4jmYpRyDAYSUcaT156zVhrroXJyofY5rO0XzAt4c9ulO9AvdPfBRFOaLa3ezK4wX2TV10JE+EaEKBtUbzVqq3lygE5doF05Efq91zmPqfM1NfA6a02pfQ4Nvl4rUzNlxVL/G3UAwMb10Gd9AfnP444MKP3684zHOOoUyDvTrcccPLzVb0n17gf55QdrA0sbtB/mG/rmZutezMmN6L5JFdWzD2TxfOfG1mobVm2xpqaSgzQ3G12EAaB7T2iX3QZZs8JoTNHo/GBA2zN4aqgqLLYCe7YSA6pPf2N7SRnQqYvzpI4x/i5y84znYG8LUF8XVTBQ1q6CPvXW4NqvNuqwE63AYLoEA83fRacuwR8qtYGKtTZcrvWhvDriJGjHjIOM3BGyeAHUmL3DnEhE0WAwkIgylmxcD33aPcCyRfG9cECmi3b6RekTbDOzK8wXxV4vsHkji/ISZQMz07eo2HjOGbYdtEdfB0SHfPou5L9PA5UbHceqg46FGr27+3NUc3AwUEQg334CNWAbyA9fAmJNg5NP3gE+eSfk8OyBQABQ/Qa3+i2pI04yOkSWlAF1NVB7HtTqOZQlfIEOaaiHWr3csS1tDRwKfPmhY5Pa+2DIWy/517XLboV+/83WARVrGQwMZeN6I8OuoBAo7QA1ckeokTsa+3zbUL0V6uBjobYfHXx+7/7AD18CgLNG3Y67Qrv4BmDg0OBGNC61HNVxp0G++wzaCX9rdchKKSMAWFNlBAOjID98ETYQCADomZpmdGGZwcDAD8NTpdBWa9BXN1INGQk1ZGSKBkSUnRgMJKKMJLoX+rXBXQDV+KtivnZQgdvAT51TYfBwYMlCqD0OAABjat7AocCyRZBF86B22z/FAySiWOlP3Wcs2KbWqhzfS7Xu5RAAUrHOWPfVC1V7HgDVx+rqqI47DfKmrzZVbTX06U9A7bCL1VV9zveQZ6YYWTXb72Kcs8tekRX5DxRJMLCgKHTHVspuZuBv3mzo82b7tqX3Ww81eg/Ivx8K2LYn5JcfrQ8ee/Vz7JcN69pNkEJqqyGfvQdZsRTakSe1/oHAel9Tou49XT+w0G55EKhYFzJbVA3YxsoMtE0tV5oHGDXW/TE7dAy+zjYjoR351/BjtfMHA6MsxRJifrI68GjIzBnGcqeuEF8QFAt/ie76cSJLFkK++QTqL3+HKiyC/tgk30DT5INve+OYwuiakBBR5NL7LzIRUQjyQ/AbV23qdGcHwzhRAS/8U0G77HYj+6B3f/821bUHZNkioKKVT6GJKO1JSzNgNixwewPatdz4amadmMcE1GXSjvwrZOA2RubS2pWQtSshM2fA88TbAAD9wzesg3/9EQCMhkQ9+0K++xTIL4QasxfkjedbH3S38ki/PWqP3KYbpnlmoHJrFlbeB2rEKOPvLRCceWYG6NsBeeVpyDczAQD6T9/4n1dC0b/7DIAR1HOjyjqGLx3Q3wo2qkHDIhqjKikLjsnlRxlQMl9LRpkZiGZnV141dh9gm5FAnq0WdWkZ1DHjIC8+CjQ3QVqaoZL8/0KfdDUAQL54H9pDrxiBSQBYF1lH+YSzv5Z36ShNRPHBYCARZaYFcx2r5qeb8ab2jaH2YByp/HygzwDXffL2S8DRpyR3QEQUX01N4febBfNrqyG1NVb2oFvwIicvaJN4vZDvPwcWLwg+vnd/aEO3A4451ThW9xp10Hr3hxoxCvqT/4IaOBTyxftA7wFQBx4N1X9I+pRPoLSkcnKDgzJmzcsMoV010bjPR+4Eefe/AACladBumgL9tkuNg9avTuEIk0uWLnSuN9RDFbgH2qSlGfjpOwCA2uPANj2eKimDOusyoKU58uxLXxdrhzyXxnDh+D5kkfpaRPUs12B15UVhsdFdF4DM+d76v1DSAWrn3YxgIABUVyVtBoq0tEBefca5zZexCADKly2eaqqgyP/zCnV/EVHsGAwkoowkZkFqAOjVD9qhJyTmgbr1TMx140A2rLWWU/DJMhFFT1YtA+rrgb4DnW9ymq1goHbBhOATi0uMRga6Dv2f4/ybXT8EaQru2Kuff7xzg1KACNSeB0EVlzp3aR7H1F7PhLuNsR99CpCXz+caiozbvdkYfG+mMzV0O+PrsO2gTr/Q3xhB9R0I7R83Q596K+SHL+FdvwbaNZOgog06ZZqAOqTy8dtQR53sfuyKpVZN0giz+txou0dXBkV17wntqolAfgH0Oy43NrpMHQ7Ld+/KrC+AsftEdIqIQObOssZhD6wpW13DkjKj/ENRMVBXC/3ZKfBcdlt042sj+eJ9R/APgCMLXDvrn0kZR6vsfxsT8EE/ERkYDCSizFRrdLpUBx4Nddxpcb+8Ousy4NcfoQ44Mu7Xjhvd1k20sSFl06/YzZgoMrJuFfRbL/Wva9dMsrJdmnzTy/LyoXbeI+hc5QsEOpR2cH+gnn3DjkP95UyoETsYyxHU/fOf55aFSBSKrcZbRukzEFi1LOj/l7ZPwEyBQUOt5RVLIF99BOy0O6AUVMfOSRhochmdgTc6N9ZUBR+3ejlk4a+Q6dP825L9GsEM4mp3PgY0NUb/3GVmXs+dBdm0AapL99bP+ekbYEulsVzaAeqkM619PaxsRX8dWLNh1Pw5YTMs42r5ktD7+g+BCvU3JdnsTVb44RNRwmitH0JElIZqfMHAsfsk5AWUtvv+0MZfBZUbPN0uXajD/2Kt2KemJJEsXgD9qr9Dn/VFSh6fKJPIsj8c6/rkCZD1ayBbKq1pwnlhnnP6DXKsqiPdM3JUl+7Qbp4C7Z5noXbZK3j/djtD9RscVSCQKGoZ2uVeu+g6qN32g9ZKtpYqLoU6+3Jrw7JF0K8+E/qNFxhT7QOICKRqM0RCdJlIEf291+C9+eKQf8dl1Z/Q//cKsGaFkemXkwMM297YN3MG9K8+gtgym/VnpjgCgeg/JKHjD0d17+VosBQx28wLbN0c0Sny49f+Ze1vFzsCwqq8N7R/3AzthvutbbvtZ51ryyhMJNmyyXr8g4917NNOvygpY4iEKusIddxpUGP2Bga615skotgxGEhEGUMaG61Omr7MQJSUpW5AKeZ4k5+iYKD+8J3A1s3Qp92Tkscnyiguxdn1G843ao+ZhedzQ08z1C6/3VopKYXa++CQx6o+A403o27ZWW41tYjiTHk80C67FSowoy7Nqa49oJ19OVTf1oNI2m77QY07HwAgvmYZaKh3bTwh338G/YozIF9+GM/hxkTWrYK8/hywZgXkiXvhfegOyJzvIH/M9x+jPzMF8uYL0B+4ydjQs68zkPXcg5D3XjWWW1qA5YsdjxFVF980of31LGslwqntYn7fO+3m79Rup7YfDWVviHL6RYCvsYq89WLbBxsh/X+vAPPnAAC0K++E9tez/dO31SnnOsaWDrQj/2p8KK95Uj0UoqzFacJElDH0u68BViyFdt2/rBdnJaXhT8piSimgS3dg04aUBQMzrf4TUSqJLxioDjzaWbepeiv0l58ylsNkI6viUmgP/RdorIMK7Gga6pxDjwcqK6B23gP6u/+F6jsAyq3LK1ECqJE7AcN3MJrPZCnVoWNQoxSZMwsYNBTKN2VfRCBPGVlh8vzDkN33T4+ZB2YHc9PcWdB9WWratLeMoOYK39RSc0ZG915Q5b0d37N8+ylwzDjAlnnml4EZomrUWCNQ9+cfEb3Okeqt/q7S2hn/gPK0HsBSefnQzr4M+o0XAhXrIM3NCX1uljdfsFZ69AYAeK69x8gWjPDvCRFlFwYDiSgjiIhRjBqAPvEKa4ev41u7ZU6RbkxMMFBmfw3ZtAHaIce7H+AyFYqIgomIv16T2m5nyPIlwGIr+8a/vGFN2Ouo/HwgP/ImBaqkDMrX0dIzOrgWIVGiKc0DeHIAb0uqh5IYLtm38uwUCADt8TehNA3y0VvO/e+/Dux/BOSdl6GGjHCdzp8Msj7M801tNfD7vODtPXoBvfo7t21cD++jd7mWHki3jLOI+V5fSWND6x2Fl/i6tPfqB1UcRX3CruX+RX3SVdBuuD8h9RWlxdn4BR2s4J/qmJxOxkSUfjhNmIgyg+8T6UDtvnFFp64AAPk+MTX79McmQ/77DGTp7yEOsBoaNIcrTE3U3s353sjiLSgEhoyAduRJwM57GFPKiLJdNv+t7jMQ6N7Tvd7nwrkAAPnsXcdmefslyEuPGzX3Hr8bEtgcKAlk43p/tpja+5DgA7Zugcz/OXh7915QRcVGx167n761ss8KCqEOPxHaPc/Gd9DJlF9gfG1sgP7vh6C//lzIQ+UPIxiohoyI6iH8zUQAYMVSyCtPRT3MiGy2Mja1y27la2ciAsBgIBH5SHWVUQTaN9003QpcY31wrS0C1KgxAAD5+du4X1tsU4/FpdaZscO6T7Y8NSXuYyDKFvpHbwIA1Ji9oQqKoLYbDc8FE6Ad6+yGrk74WwpGR5RgWvYGH5SmQbt6ErSbpwbtkx+/Nppr+KaQKluTBvnhS2v5nemJH2jg2GZYj6mOOAmeJ96Gdt/z/imk+ouPQCorgs5TPXoaX4duB+22R9wv3q0c2glnZHRXZeULBspXH0G+/BDy3muO10V28uciY2HQ8JgeUz5+29Hko83X2boZsmyRtcH8QL1zV2PqPhERGAwkIh/94TuMItAvPQ7v7f+Efve1MX9SLfN+gvemi0JnlUVzrc8/iPka2UjtfoCxUF8HqauN23Vl9XLod9i6JFZtafWchtnfQDbH/iKWKCtVbwUAqF32dG7v2cexqtm7hBNli7yCVI8goVSHTlAujXnkyw+h33WVddxeBwG9+wcfN2M6pMroWqt/8wm8E86BLFkY93HKvNnwnnsMvLdcAlmzwhqXb6qzKu1gddL9Yz7gy3jzlyQBHA2IVM8+/uChg/34TFVQZHy1B9U2bYA0GM1hpLkZ0tJsdI5ebpSxUQOi75ys/u8Cx7r8/F3bxmujXzce+sQr4b3vRmOD2XSvqP3W2SaiYAwGErUjsvR3iK8wdBDfi0759hOjNt/i+c4XQG2gT7kFWLsS+uv/juk6ACArl8Z8jWyk8guAIl99mhVLoH/3qZGFECP9gVuA9autDTVVQceIS71A/eO3grYRtWeyZCFk3k9Ao69bcLHzzZjSNKDc5c00URbRLroeKOvor1+ZrbRJTwIdu0AdeoK1ceUy/6LSPNCO+z/Xc/XnHjKeL555ANi0Ad6pt8V9fPrzDxsLq5cbzTEAaBPudh6Ua5u66qtHrOx1g0s7BBzvMj3aDKRlMLXHAUHb9Neeg37JKdC/+xT6jRdAv/0yYO1q4+eUlw+U93G5UiuPs+9h0P71HNBngLHBpRN11Jp8f28WzIX+w5cQMxgYTT1DIsp6bCBC1E7IvNnQp9wKANCungS1zcjWT9q8se2PZw9IBRYujuY6SxYanehcilyro09p83WzSodOQF0N9H/dYKyvXQV1/OmxXTNwmopbMPDTd4O3/fIj8JczY3tsoiwgtdXQn/wXMO8n5w6XDCm1856Qd19J0siIkk8NGQHt3ueyvlaZ6tIdnnueAQDomgZ571Vr3+EnGgvDRznP2f8I4+/pbz9BX73c2lFbjbUX/hUtOqBde3d8ug8HBpoGDTP+2eUXAk3ODxXVLnsZWYAdOgf9DtXYvSGrljm3ZfD0YJMaPNxoUldvm3Xx648A4O8MDQD6Mw8YC0NGRNRFOOhxlALKOkEN2x6y6k+gthoi4vp/Rf/mE6iefaEGbhPyeoGzemTaPUB/X8ZiMTMDicjCYCBRFhIR6HU1kOZmICcH+n+fgXz4hrV/9Z8RBQOlqan1DmqhHv/B29twppP+36chH77pvnPUWKgjT475MbJCwCe98tM3QKzBwAAy7+egF6fy+fvBB8ah1g1RNpD/PhMcCASsovQ26tDjIYvmQY3ePQkjI0qNbA8EBrFPBx4xCspXH1QVFEKbOM0IuBUWQXXuCu9vc4xO4ps2OC7R4pt+ql/4F2j3PBtTkE1aWoKCgeqAo4J+L9r4q6wPFwEjE7BzNyMY6EIdegKg61bzEHNbFlC77ev6wafD8sXGsYOjax4SpNDIppQP3wSKS6GOOMmxWxYvgDzzgNGletpbwJIF0D94E6rvAGjHjLOO+8+04Gv7pn6rHsFT2Ymo/WIwkCiLyOZN0O+/CVi7EqsBYNAwaBdd7wgEAnB0gAUA8QZP9wQAtLRtuqm88CiwYK61IUQn4FavEyoQCECNGtumT2CzUmGxcz0n+uwBEQG8LVA5ue4HbK2EfPsJ1B4HWtvcppv4aheqouLgfURZRH6fB7Q0Q227kz9QLroX8um7kFefDZ0R7RYMLCqG55pJiR0wESWVGrkTzBZbavtdHK9ZVLdy57Hb7Qz5JHgGhJ28Mx3qtAvbNBap3gr9+vODx+jywbAavoMRbFo0DyjpYDSdyM8PeW2lacDuB1jBwN79sybopP5yJtTQ7aA/fnfrBxfFODXaVmdR3ngeCAwG+prQAL5u1O+8bCzP+Q5y+ElQucbrt8DO1QD82Y0qxBR1ImqfGAwkygD6lx9CPnwT2gUToHr1C33c3ROAjeutDUt/N+r/BaoOmPIZqjZgU/TBQNmyCfJFQMZYffSNLVy7GffqB5gFr7OgHk28qMJiOH5auSECemHoD98JLP0d2u0hOgMCxnQnezCwtsYawy57Qv0+D3r1VmN6OYOBlMVkzQojc0asD1bU6RcC69cGf/gSKMybaiLKHqq0DNpNUyDffQa154Hhjx27D+STd6wNJWXB5TmibBImDfWQ7z+HGrkj5PvPHa/F1BmXACJQnbu5j0cpYNj2kT9Ynu1DyCwJBAKAyssHdtkLWmlH6PdeF/7gWF+X5jubrkhzk3NquC2B0wwE+tfffw3i8UAd/hcjk7N6K9CzL7B2pe18DUrjh+hEZGEwkCiNyZZKYPNGyL8fAgDoz06F57p7Q59gDwSa11g0z1oZuSMwfw7kt58gI0YBTQ3GC8Rtd3K/XlsaUaxeEbytLcWQXQKRasddre53Ls0r2q3AwFuUwUDRvYCvsYz88CVQGaJW5LrVkDnfQe24m7FeZwQDtRvuhzZgCNTEK4xgYGWFa7dEomwh33ziCAQCgDwfHEhX+x8Bbdz5kJ+/g/7IREDTQmffElHWUX0HQvUd2PqBg4YB2+8C/PojtJunGk05KivQa5+DsOo4o3yA/PAl5Kx/Rvwcoj98J7DwF7h8tAptr4Oj+C4iYAtaqa7lYQ7MUIOGtnqIKowtGKjKezt/V9VVQOeu/lX56uOQ58rbL/mvgZYWAID29384OlkH/s0iImIwkCgNyfIlkPlzIK8/59yxbFHIosKybnXQNgD+4tXquNOgRu8B/cYLgWWLoN9zrXXQwl/cB+ISDJSFvxifJo8Y5XICIF99ZCx07wXtmknQr/gb0NwEaWmO7k1wbfDUYjVwqOuL2navyDldV3WIsqbQ5kpruWprUM0iO/2RSdAe92U+mb+jkjIAgKdbOZqXLoJUbmxTrUmiTCHzf47swO5GhozaaTdoF10X9H+ViAgwMvE8/7jJua28N1RuLtQeB0K+mQkAkB+/gtptf9drSEsz5OO3oXr2hfz4dejXdiFev8XE/iFkFn4YqHLzoP3rOch7r0ON2AGycb3xe1gwF/pjvhIPebFlfavhO0C77WHoN10EAJDF84FttoX+0O1Q240Gfv+11WvIj18DDb4P4Dt1de7cbueYxkdE2YfBQKIUkRVLgKZGqCHOei2ybBH0iVeGPnHebOPTY/s5Lc3Q773ev64OOQ748iOIfUrI7gdAde4KlPcGAgOHW3zBoMHDgSULrevOmA7Zfoy/a5msXuEvKq3d9jBUz75B45A53xn7/3aRs2tZ5Uage8/Q31cgc3pMcakVdOo7EOrEMyAL5kLtzEL7fgEFxSXajM7NFda582Y7PokOIjrQWG/UnfR9+owS4/fs6dDJWHcJ5BJlC1m7Cljp65zp8QCaB+jRG7B30/TkAIOHQ+15kH+TP6OWiCgK2nH/B68ZDPx4BhAqGPjlR5DXngv5oak64iSoA48OrjMcB0rzQI3ZG7K1EmrM3nG/fjpQZZ2gTj7bWPZtk512tQ6IQ7dn++tq+fhtyBPGbCCxl/zp3NU/g0MdejzkA1uDwB++tI4LqOusHe6sQUhExGAgUQpIYwP02y8DAGj3vwBVUmYE0v79EOTbT8Of+/VMYMSOUDm2/76LFwBbfQG9/EJox4xDgXhR+9EMAEZwUJkBnk5dg4OBPqpHb4gtGAgA+lP3wXPHo8Zj+wJ9ACArlwUFA7F1sxEg8uQAQ7dzZDDqD90Bz20Ph/3eHN+n+Vi9+0E75ASgpQmqS3eow04EDjsx4uu0B6pTF+eL/8aGsMfL778COblQg4cbG+wNXpYtsmpI7rir0Q01L89Zq+jPxYDZdKZjZyhfQwR/05CGNkwLJ8oQ9jdb2oO+uk1Kgz7pamD5YmiTnoTq0j1FoyOibKM6d4N2x2PQbzgfWL7YaF6klFGbT/NAlZZBRCAfvxV0rnbnY1Dde4WcVRJP2virWj8oyyjNA+38CZBli4Ch28XnokNGGK/rfR2Agx7z4GMhLz9lLO+yF9SQEdAfnug8KCfXCE4O3Q5YNA/qqJOhhm4bn/ERUdZgMJAoFexTNyrWAyVlkH8/7BoI1CY9BdWlG+TX2dCn3gqZ/TVk9tdQJ50F7ZDjAAD6Mw8YBw8cCu2SG6EKCtHp0pvQcOQp0Gd/DbWr9SmyKi4NPdXWrd6JrTaf/PqjtX2DS9e75UuMrx07B7/otBcxboVs2QSZMd0Y75BtoUaNifjcdql3f0BpVj2YpsaQh8qaFf4sUu2eZ6E6doaEyOTTDj0eOO8aYMMa6Ddf7N9uZocCAPoPsY43g4FtqRFJlCnWG8996i9nOoq7e264L1UjIqIsp3r08meEuTWy0CZOcwaPOneDduG1UGapggQHAtszNXoPqNF7xO96+x0BWbzAfZbFoGFQY/f1BwPRqSvUgG3geeJteG+91MpQ794TSilo510F+XV2yKnlRNS+aakeAFG2k8YGyIK5ju64Mud764AtmyAtLZBvP3G/QIeOAAC1/Wj/9AQAkP8+DX36E9Cff8SaLrDXQVClHYxlpaBKyqDtcxiUvXtlgbNbmUPnbtCunuSc3tupCwBA/+4z5xTiBXOd32dzM/RH7zJWPFa3Mu3iG1t/3AD6vbZgU17s0y6ynepWDu3mKVAnnWVsaAwOBorXC+9Dd0Cfcou10VcbUH78yv3CHTobGahhOuTZi5AzGEjZTnQdstD33Od7riUiSgY1amzIffp1440FTw60aW/BM/kpKNuHdZQ5VHHo2rLahddBlXWEOv0iqFPGQ5nlWQB/SR8AgC87XZV1grbnQVAedhEmomAMBhIlmLz0OPT7boT87xVIUyO8k662mmwAkIp1kOce9K9rl90GddxpxkpRsaPphtrnMOe1Z86AfPG+tX/XCD75803pdKN69YPaZiQ8D7wI9X8XGBurqyCNjZDnfVN8zfp0fyyAmHX9AMi7/7UuZK9H02+Q8bW5yREQDUWam4BNVldkNWhYq+cQoHr3hzK73TW5TBNeNM/oGGzvFOzrBox5P7lf1Pxdl3UEbC84HWydqJWvOYI01EcxcqLMIIsXQD/vOKBqCwD4P3ghIkoGddiJjg9bXeXnMwsw0wXUdFRHnQI1ek+ocef7g3/aPodCO/Ao53m237t24hkJHyYRZT5OEyZKIFm3yuoA99aLkLdeDD7m8/etKbeDh0ON3BEYtr2R4j9gG8exKi8fav8jID99Z9UINPUf4swADMVtKrBpG6ueiBoy3JhOXFNlPJZv6ql29SToU24F1q+GftlpQI/e0G6eAnlnuv9c7W/WlFKzuQS8XmDNita7zG3c4G9MoZ0/ARi+Q+vfExnyfIFet8zAjeuDt9XWOLr+ahdMgGzdDHnpcaPQuG8KpMrJgXbHo8BvP0N/bLJ1/NTpjmmSmtkpNQ6ZgfrMGcDmjVBHngwV7p4lShL5LqCMQ5gPVoiI4k117gbtvueBtauMGqUAtAl3Qz79H+T7z42D7PV9KTMFNIVTO+8O1Xdcq6eJrf6zysKOzkQUfwwGEsWZrF8D/ZWnoHbbz8jGCkGdfpGRbWcGAvMLoF1pFABWHk/IbmzauPOBcedD//dDkC8/tK7Xo3dE41P7HwH5+G0gIHtLHXeaM5hYUmZ8rd5qdc7s0h2qWzlUv0GQ9b4mJOtXQ37+znktMxsQMIJFg4YBS3+HLPqt9RcoZmfbXv3iWoOlXcjz/f5cMgMdU9NNdTWQzZus9cEjoHXoBOx/ZNChqqAIYps+rsbsHRSki1cDEVm9AjL9CWN50W/wXHdvTNcjipVUbTY+uDEN3dZ4XiMiSiJVVAIMHm6UYPE2Qw0eDjV4OPSOXSAfvA4EfIhMGahzN+e6r+5ja9SIUZCfvnGW+iEiCoPBQKI4kvVrjG5vAOSXH1yPUXseCHXqeVD5BfB+8LpV8HnANs4Owa3Q/nYx9GHbQ77+GFAa1CnnRHSeKusE7YGXoN9xOVC1Gdo/b4WsWQE1dh/ngcVl/kX9sUm+bb4XGF17OI+dPyf8Y/bqB1n6uzUtNQwxp7Ga3Y8pcmamUmODo3OgLPvD6hBsV10F/Xlbh+ei0HVqABgdovc4EFKxFurv/wjaHbeagTVbrWW3cRMlmf7cQ/5l7c7Hobr3TOFoiKi9C2yspk48wygV0ndQiDMoUyilgFFjjdIuQGSzfgCovQ4G8gug4tXVmIiyHoOBRHEiIv5AYCB1+oVQZR2BETs5/qhrF14P/RZjSq129ClRP6a2677ArvtGfZ7yeKBddy8gujH1uO/A4GNyc4NPrPYFaQKKG8vXH1sr2+8SfJ4/SBTB9BVfHULW42oD897SdaMLXUkZZN5sY1q3m8oKYPli/6rr79xGaRrUmZeG3B+3YGBt60FjomSRhnrA/uFOt/LUDYaIyIVSCtiZsymyhXbeNdCfvBcqilI5KicHand2DSaiyLGBCFEciAjk/desDTvvDnX4icZynwFQu+0PteNuQZ/uqd79oN33ArTr/wU1bPskjtgI/Ki8Vj5tHLmj85wxexlfw0xZ0P5+SfBGXzFk+eANeB+8HVK9NfgYkxkI4jSH6OVbHZvNDsH65x84DlHnXAF14NHGMZs2QI0YFbeHd5smLLXV0Gd9AWlujvg6snq5cz2CxjNEiSKzv/EvaxOnsTg/EREllMrNheeCa6G5lG0hIooXZgYSxUDqaoC1qyBLf4e8/m9j4zYj4bngWmP5hNa7eanSMqC0rNXjUkG75CZgayXku88AjwfqoGOMHaPGup8waixUmUvX2SJbZ7RffoD8ZxrU+Kvcr1HrK4Bc3MqUVQqiPB6jltmi3yBffQzvvJ/800wAANvuBG3XfSHde0FmzgB+/xXi60zo7x4dA38DkZYWSG01VHEp9H8/BPz0LWTY9vBceWfY88XrBRbNg7z3qnNHSzNga1RClFQb1/kXFbMCiYiIiCgLMBhIZGOvsxYJ/YFbgmqaaYedGOdRpY7KyTGahhz5V+d2TQNG7gTM/9mxXbvwOvfr9BkAe26X/PAl5LQLjELYAcQfDGRmYFuosftCFv1mTP9dHrDPzOjsY2vi4vUaX+PQGVUVFgGlHYDqrZBffjSmq/z0rbHz91/9AcJQ5L9PG0FKAMjJNYKAgNHJmsFASpVNGwAA6vjTUzwQIiIiIqL44DRhIgBSXQXv9edDH38s9Jceb/VY/auPjEYX9kCg0qDd/wLUDmNCn5xFVJ8Bwds096cUNXQ7q9Otj37pOHjvugqyeL5/m4gAS383zmEGTpuo3fYLvbPACPip3DygYxfneUNGxP7YmmZNO67wNcaxP87iBWHP9wcCAaDvQMBjfF4ls76A957rIBXrQpyZONLYAP3pB0I2BKLsJ2bpAtYxJSIiIqIswWAgtTtSX+cIKkhtNfQbzgM2rDHWP/0f5M8/jOXGRkhDHWTjeuifvANpaYF+342Q5x6Efs1ZjuuqnXeHKknP6b6JoI4+GeqgY4HRRsFqtVv4osXaP24Ozu5a+jv0yROsmnCN9cCWSmN5yMh4D7ldUPkFQGBAesfdgB13hTr4OOu4XW3do8v7xC/4WmDULZQZ0yG6DhQW+XdJZUXI0/QfvnJu6NDJH0CWlx4HFs2D/shd8RljFOSdlyHffgL9wduT/tiUJpqbjK+t1VglIiIiIsoQnCZM7Y5+x+XAhjVGIfhu5ZB3XwXqnF1u9TuvgHb7o9BvDKijVlMFrFoWfNFu5VDnXJ7AUacfVVAEdfLZRp23/ecDA4aGP37YdtAefNkIAN49wbmzeitQ1hFobPQdrOIybbW90s6+DPqEc/3dm7WjTobqP9h5kH26btfu8XtwM5gLGN2Kfd2hAQCNDa6niAhk2t2ObWrbnSDL/nB2oHb7v5dg5gcDFJ40NwMrlgADt4HSPKkeTnw1Gc9LrTZcIiIiIiLKEMwMzBCyYS28t/8T+nefJu0x9e8+g/fWSyEb1ibtMRNNKjdaGYC//Qz56RvIh28AANQJZ0C7+Ab/sUGBQBjZToHU+Kuh3fowVE5ugkad3pTHAzVs+6BOySGP3WYk1OkXObbr15xl1ApsrDc25BewY2cMVFEJtDsehXbpzdAuuzU4EAgAtnqNanDsU4RNYuskrF97rhHoNZnB3kArllhjOehYqJPOgtrrEGfjGfOan7wDqdoct/G2KlznawJgBAL1i/4CfdLVkPdea/2ETGPetwwGEhEREVGWYDAwQ8hbLwIrlkKeuj85j+f1Qp66D1i1DPr150F0b1IeN5HE64U+4RxrQ8Va6I/5spG694Q68ChjemXHzq1eSx13mrU8bFuo3PYZCGwrtXvAlOKWFuMeb/BljuUXJn9QWUaVdYTabjTUyJ3c93ewuj6r4TvE7XE9x4VpstAUHAyU5UuMbF0A6N4T2slnQzvkOKN5TYfgztTyn2nQn3ogTqONgLcleY+VAUQEUl3l3PbufwHfVP+sDAaa04TZxIaIiIiIsgSDgRlCWpL7hlT+94pzw/Il7gcGntfcbGQUTrkV+nuvOvbpX34I/cM3jWmlSSYtLZAZ/wFEt7Z9+KZ/Xbvoeqi8fCiloF1wreNcdcIZ0B59DehtdWBVex0M7aYp0CbcDVUWHLCg8FRuHrSbp0LtdbB/m3z6LvS3XzJWIsgypBiV97GWO3WN22XV0G2h3fe8o1agX1PwNGH92SnWSmCDBpfMQABBXawTQVqajQxEXW/94HZEnnkA+uWnQf/0XWvbmhXWAZ7seVkhIpDVy61mOHxeIiIiIqIswZqBSSQN9fDe9k+gWw94/nlr8P6qzdAfvANqzN7Gm/PZX0P97SKoohKokjKIeVxLi5E1k6hxrltlBM5s9Advh9ptP6hhOwAjRwG//Qx07Az56C2jXt7oPaH6DoQ88wDkhy+N68ybDR2AfPUxsGUj0GRkV6juPYEdd/U91mrIwrlQ+xwa9zpT0lAP/ZkHgJ++DX1Qbh60Wx9yNE9Qg4YZwb+Fv0KWLIA66BionFxo1/8L+pRboXr1M7KqXLKWKHKqzwDgpLMgX31kbZw7y/jKeoGJ170cGLCNkdHVqfVs2Gio0g7QzrsG+gM3G+uj94TM/jqoZqBUVgCrbYGkgMCbKuvof95LNnnyPsic7x2ZgSLSrqevy4K5kG+NUhXy9kvQN64DvF6god46yKXLeKaSz941mteYOE2YiIiIiLIEg4FJJPPnGPXqNqyB955roYbtAHXIcVAFhZCWZuiPTQb+/MNZsL64FOr0C53ZFvV1QGniutbqzz/iX1aH/wXy3qtA9VbIR28Zwb+BQ4Fli5zf24dvQrvoen8g0L/99X8HXV/WrgIGDwfy8o1gwaYNkJ++hXbuVUBtFZQtY0lWLIF8+ynUEX+FiuJ7lnWrXWv+AYA24W7ok642vr9d9nLtoqpycoHtdobabmdrW24ePFfeGfEYqHWqqBjag9OhP3Qn8Puv1g4GAxNOaR5o190LiEBp8c/mUtvuBM8TbwMA9E//B8z+GtLUaHQYBqA0DfL5B45sXbXdaOc1TjoL8tl7cR9bJGT218EbW5rb5VRRaaiHvPIU5MsPrY01VUZ2dQBV2jFp40q4lQENa9rh756IiIiIshODgUkk9gDaot8gi34DPB6oI/9qBNn+mB98zuo/jQV74f2GxAUD5fdfgUXzAADqtAuhdtoVsmAuYA9QBgQCAQDNTf4sIOy4K1BXAyz6zf0xfv7WyDw06zABwIK50K/4GyA61J4HQo07H/Lhm0YdOQDysRFUwOg9oJ12IVRJ6O9fRKA//5BzY69+QGERtCNPhho8HBg1FlizAuqUc9wvQkmjCoqgHXca9MnXWBsLQ0wPpbhSShmdmxPNDO5WbjT+n/cZAO2ft0K+MoJL6rjToPoPMbKO7ePLy4c69ATIB68HXTIlWXoNDe0yICTff+4MBIY71qUuZMYSW15qfkHwNHYiIiIiogzFYGASiS/I5tj25gvQc3JcM+gAAEsWQnQd8s1Ma1t9bWLGt2oZ9Huv969r+x4GAPBc/y/I77869tmp3ff3Tx0DAG33AyCL5xvBTgAYui20g4+FbNkMefFR92Ai4M8Qkq9nQr6e6X7M7G+gz/7GeNxjxwFlnaDKe0MN3c66zHMPWoHIvDxok54Oyir02LoGU+qpISOgxu4LmfW5saGmKvwJlFH8033N//sLfzECfFVbjP2j94Qq7+16rvaXv0N2HAt98gTnjuqtQFnHhIxXQtUJrK1KaFZ2upIFcxzravcDIN9+4n5wY3BdyIzV0gwAUAccBbX/kVDMWCYiIiKiLJE9lb7TmFRtwaa7rweWLAQAaNfeA3TtYe1/9Vnr4IBpcgCgX3O2c0N9XfzHKAJ9kvVmWx14tGO/GrY9tDsfd5603c7QJk5zfC8AgJGjoA47AWqfQ6FdORGeq+6C2nE3oLjU/cG79wo7NnXsOMAlE1Deegny/MPQ77kO3tv/CWlqhP7DV5CvP/Yfo/3r31FNL6bUUSfb7vN2XJctK3XsErRJ3njeWuneM+zpasjI4I3rVsU6qtAW/uK+3Re8bE9kUwXg+wAGALRbHgR6hHnOzqZgYLMRDESPXiGD1UREREREmYjBwCTw3n0t6j7/wFjZbjQwcCg8dz0BtfchQcdqp54bfIEtm5zrdQnIDNywFmi0isBrpwSPQ3XvCbX7/oCmQZ15KTyX3gLlax4Cj5Fkql01EaqgCKqsE7TTL4IaZmXsqWL3qZ/aKecCQ0YAg4dDHf4X6/g9D4R2z7PQjjoFnvtfgPbYG9BuewQYtn3wRVYshbz6jH/aIeCbeljg0tGU0pIq6wg17nygsBjaX89u/QTKHJ27hdyljjstspqFAQFB+erjEAfGTv/fy67bJVRWcxbTp/qaXXXpDu3h/0L17u/sPt2rHzByJ6MOLJBVWb3iywxETm5qB0JEREREFGecJpxgsqnCkcGinXulv86VOnW8VYfJkwN1zKlQ3XtBe/BloKEO8srTQQ05AEA2rkc886akoR76DedbY7zh/pDHamddBvnbJY5uxqp3f2i3TAVy86C6dA/9QEUl1vLwHazsm46d4blmsjEWXYcasA0wcChUJ2c2kfJ4gJ594LnyTkhjI+SHL6DK+0C/9zrA64V8+q7je1D9B0fw3VM60fY/ArLvYQlpaEGpo4qKocbuA5n1BdChM9T2o/1dpO0Ng8LRLr0ZWL8a8vXHkE/fhXz7CeS4/4MKE2hs83i7lltlDmzko7chBx1rPBe1A6LrwBpft+f6WihfN101dh/IMw8Yy/sdYfy/rawwsti3bDKex7Ph/3Azg4FERERElJ3iFgx8+umn8f777/vXe/TogQcffDBel89Yqks3eG59CN6bL4Z2wQSoIis7TuXmQbvjMcj8OVD7HgqlGW8wVUEhUFAInHmpIxiodtkL8uNXwPrVIR9PamsgM2dADRkBjBjlKLAvVVsgb74ADBgCbZ/DrO0fvWU9xuF/aTWIZg8E+rdF8obe1mVSO+9q6JedZqzYphkrTQN23r3VS6n8fKi9DgYAeB57A95bLgFWL7cO6N2v9fFQWsqKIAIF0c69ErL/kUadv5IyfzAQ3XqEPc+kCgqB/kOA+jor8L9uddisw7aS2mpjIS8PaLI1OtpaaTRHai+NJKq3+hfVSWdZyzk50Ka8ZJS+GLmTsbGsE6A0wOsFarYa6xlAdB3y3qtG5uNu+zl3mpmB7bBpDBERERFlt7gFA5cuXYoJEyZg2LBhAACNb+j9VO/+6Pu/H7F27VqIvTshANWjF1SI+ksqNw/ag9Mhc2ZBjd7TCAz++BVknTMYKKtXAPn5QH4h9MuNAJvACB6q8662jvvuMyMT8csP4f3lR2DlUqCh3pp27PFAHXZi/L7xwO+nSzeosy6DKi2DKimDdtcTQHMzVGHsU3m1Y/8P+iMTrcdiJgdR2lFDRljLBx0DbKkE+gyM7hrDd/AvS/XWuGZJ+21Ya3ztWm5lxhUWGfVaa53BQGmog/7QnVCDR0A7/rREjCZ1Kjf6F9WeBzl2qaISYPtdrPWcHKBDR+N3unlT2gYDZd5s6NPugdrvcKhj/g/6g7cB8+cY+0buCGVvSmM2EMnlJAoiIiIiyi5xeYXr9XqxcuVKjBw5EgUF7LYXT6qgCMrMVujRy+jIuWQBpKkRKi8fMuc76A9PdD1XfvwK3i2V0A49HmrHXYGqzdbOubOCjtcmPuHIXEwEbff9/csqsPFILHYYYz3GZbfG77pElBDayee0+VxzyrF89BZkzF7+rOp4kMYGI+MQgBqwDcQMBhaVGMHArZWArZmE/sAtRtf333+F991XAE8OtInToDp3dbl6hqmsML4OHu7IMg+pYxdfMHCjkcWZhvSptwOiQ957DTLrS2DTBmvn4gXOzHTWDCQiIiKiLBWXYODy5cshIrjqqqtQWVmJkSNH4rzzzkPXru5vhpqbm9Fs1uIBoJRCYWGhfznb+GsExvq99RsE5BcAjQ3QLz0V2j9vdQ8E9hsE1NcDFWuBxfOhL54Pz52PQz54I+zltS7xn26XLConB+rmqZCtldC23TnVw0m5uN1zRBFK5j2nhu9g1B9cvhjy32dcGx612erlgOhAh07Qjj4F3m9mQo0aC6ncCGzaAP3e640GUN3KjZp6q5Y7z/e2QD5/D9oJf4vfmFJEfMFA1aV7RL9X1akr5M8/oD88Edpxp0E76uSEji/wntPfew36a88Cnhx47poG1bkb9M/eg1RWQNvzIKC0zPjdmuyBQABYuxJK7WFc6+uZwIqlxvbcPD6XEgD+baXk4v1GycZ7jpKN91xqKQmct9oGX375Jd5//32ceeaZKCsrw9NPPw1d1/H/7d1rfFTVvcbxZ+1JSGIAgyQhQEIIcjMqiEiwwSiK0taDcmzRU1JbERArWlFblWK91VpBaPHC8YKWyLEoh1KrlVYtePtUsUVUoIhaQQEBw50mJBMIM6svNswkJDPkNjMh8/u+IXvtPXvWxMcV8metvaZNm1bv9YsWLdLixYsDx3l5eZoxY0ZzuxEXdt59o6pWLg95PvmsQmXc+4gOfLJGO346PuR1ktTx+5NU+cZf5Omcqc5TH5DnqA07AKA1OrR9m74ef2ngOOfPK2Wrq1X+wrNKOnWQkk4b1OR773/1j9r76P1KPvMbyrjvUfkr9suknKCdP79BBw7PqG4/6gp1uu427Z51pyrffKXOPdqPukIpwy5Quz75clrgMQixYH0+bbl0qCSpw3d/qLTxNx7zNfuX/kl7H/pF4Lj7C+/ISYrOaoED61Zpx63B2aYJPXrpxOJrtHv6z4752vajrtD+JYskSYk9eyvx5P6qfH1J4HzmzN8qKX9gy3caAAAAiJEWmRlYVFSkoqKiwPGECRN0ww03qLKyUiecUPcXocsuu0yjRo0KHB+pBO/cuVOHDh1qiS61KsYYZWVlqbS0tM4zAxvL5/PXbcztrYQ7Z8tWH1R1QqL7bMIT0+VcMUH+F/4vuNTpSH++PUamfUdVDB8lc/4l8knaUXVQ+vrrZvUNrUdLZg5oiGhmzvpr33/bV5vlu228VLZPapekhMcW1//CY9139w75XnpeknSgfUd9fWRMLCuXr8ZGJRXl5fJ+uk6+egqBkrR/ySLtX7JIpnCEPONvalJfYsVuXO/ObvRWBNoqklLkbcDPB5t/pswpZ8h+skqStO2OG+SMnSTTLScifa2ZuUNPP1zr3KHNX4QsBJpTB8l+/FHguDKvX+Dr6o3rVb1xfa3rd1sjw89HiJ+tiC7yhmgjc4g2MhcZCQkJysg49qrPiDwVOzU1VdZa7du3r95iYGJiohIT638GT1sOgbW22Z/PXDhadtP6Wg92d66e4t738HONjryHuWi0PBeNlu/he6S1H8oMv1jmlIHSoLMDBdi2/P1Gy2QOaIyoZM4YOTNL5L/1akmSb+pEtxAoSQcPyLf8jVrPJ/X/eZF08IDMf18ZchmC/6UFskv+P9jQ4cTan6PGcwLl98n/4u+C3fneNZK3QvJ6Zf8afByDXf66/JcWy7TCRzBYa6XPP5aye7qbgUiy+8vk/+XNkiRzydjgtY6nYf9NjSNz0z2yk78r+Xyyn6yS7w/PyLn+jogu//BXV8v+a63bhaHnyW7dJG3ZWP/FaSfJXDlZemOJ7NKXZCb+ROrZJ/TN8/pKndIZR1ELP1sRTeQN0UbmEG1kLjZapBg4f/589enTR4WF7rN21q9fL2OMOndm2WlLM31PlWfGPPkXPiX7+stuY9fwsy6cSbe5v/SdNliGXZ4BtAEmrbPk8Ug+n7tpRQ123mzZvL6yy5fJvvKH4GsGFki9+h19K1mfr3YhUHKfz1rz/QovkH1+rnv9/rLgLuwZWXJGXOK27yytVQyUJPvZGpnCEU36jIF77PhadvUKmXO/JZOU1PT7lO2T/4npUmWFTOEI2d/Pk3rnywwYIpVukV3+evDal58PfG36ntbg9zCOI9Wcubl6hex7b8oUXtC0Pvt9sm+9ItP3NJnsnvVf8/rL0pF/BPv25TJ7d8r/8L1S2klyvjdJtmyvzNnn1965/rvjZIpGSlnZMsbIfPMy2S//Jf3r4+Bn+eENMoOHNanfAAAAQGvWIsXAnj17auHChUpLS5Pf71dJSYmGDx+upGb80oLwzMjLZP+5UuYb5x+zwGdSTqi12y4AtAVm/M2yT82q95z/zuvqtNnP1socLgZaa+X/9c+lz/4pnTG07r1rzCyU3J3dneunuZs27d0dmInoXBV8lp7JyJKZcIvsb38TfGGNWdzHYle/L3XNlsnsGmxbt0r+2Xe5B94KmUuLG3y/Ovd/703p83Xu17+f5zauXye7fl3Y15mu2Y17o4EF0qq/B9/3rb9ITS0Gvvem7PNzZSV5nvpT/dcc2fU4t7dM9x5S9x61rq1vTqLxeGr9Q5ozxp1laneWurnI7CpTOMK9DgAAAGhjWqQYeN5552nr1q2aOXOmkpOTVVBQoLFjxx77hWgyc1K6PPc/GetuAEDMOAXnyp54kvyz3M2qzOhi2ZeeC/2CzRtk/X7Z556QffvVYPu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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b107f6e600>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock601939.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3564 entries, 0 to 3563\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3564 non-null   datetime64[ns]\n",
      " 1   open        3564 non-null   float64       \n",
      " 2   high        3564 non-null   float64       \n",
      " 3   low         3564 non-null   float64       \n",
      " 4   close       3564 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 139.3 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3559</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.498</td>\n",
       "      <td>5.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3560</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.444</td>\n",
       "      <td>5.544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3561</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.394</td>\n",
       "      <td>5.515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3562</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.384</td>\n",
       "      <td>5.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3563</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.35</td>\n",
       "      <td>5.368</td>\n",
       "      <td>5.466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10\n",
       "3559 2024-12-19  5.41  5.42  5.33   5.41  5.498  5.570\n",
       "3560 2024-12-20  5.41  5.42  5.37   5.37  5.444  5.544\n",
       "3561 2024-12-23  5.38  5.40  5.30   5.31  5.394  5.515\n",
       "3562 2024-12-24  5.31  5.41  5.31   5.40  5.384  5.495\n",
       "3563 2024-12-25  5.40  5.42  5.32   5.35  5.368  5.466"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            value     date\n",
      "2024-01-01     10  19723.0\n",
      "2024-01-02     20  19724.0\n",
      "2024-01-03     30  19725.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from matplotlib.dates import date2num\n",
    "data = pd.DataFrame({\n",
    "    'value': [10, 20, 30]\n",
    "}, index = pd.date_range(start='2024-01-01', periods = 3))\n",
    "\n",
    "data['date'] = date2num(data.index.to_pydatetime())\n",
    "data = data.sort_values(by=\"date\", ascending=True)\n",
    "print(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-01-04  19.79  19.80  19.36  19.41\n",
      "1    2010-01-05  19.33  19.50  18.90  19.08\n",
      "2    2010-01-06  19.04  19.68  18.91  19.67\n",
      "3    2010-01-07  19.50  19.64  18.00  18.63\n",
      "4    2010-01-08  18.63  18.69  18.19  18.43\n",
      "...         ...    ...    ...    ...    ...\n",
      "3589 2024-12-19   5.41   5.42   5.33   5.41\n",
      "3590 2024-12-20   5.41   5.42   5.37   5.37\n",
      "3591 2024-12-23   5.38   5.40   5.30   5.31\n",
      "3592 2024-12-24   5.31   5.41   5.31   5.40\n",
      "3593 2024-12-25   5.40   5.42   5.32   5.35\n",
      "\n",
      "[3594 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3594 entries, 0 to 3593\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3594 non-null   datetime64[ns]\n",
      " 1   open        3594 non-null   float64       \n",
      " 2   high        3594 non-null   float64       \n",
      " 3   low         3594 non-null   float64       \n",
      " 4   close       3594 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 140.5 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>19.79</td>\n",
       "      <td>19.80</td>\n",
       "      <td>19.36</td>\n",
       "      <td>19.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>19.33</td>\n",
       "      <td>19.50</td>\n",
       "      <td>18.90</td>\n",
       "      <td>19.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>19.04</td>\n",
       "      <td>19.68</td>\n",
       "      <td>18.91</td>\n",
       "      <td>19.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>19.50</td>\n",
       "      <td>19.64</td>\n",
       "      <td>18.00</td>\n",
       "      <td>18.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>18.63</td>\n",
       "      <td>18.69</td>\n",
       "      <td>18.19</td>\n",
       "      <td>18.43</td>\n",
       "      <td>19.044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3589</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.498</td>\n",
       "      <td>5.570</td>\n",
       "      <td>5.527000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3590</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.444</td>\n",
       "      <td>5.544</td>\n",
       "      <td>5.517667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3591</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.394</td>\n",
       "      <td>5.515</td>\n",
       "      <td>5.507333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3592</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.384</td>\n",
       "      <td>5.495</td>\n",
       "      <td>5.500333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3593</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.35</td>\n",
       "      <td>5.368</td>\n",
       "      <td>5.466</td>\n",
       "      <td>5.491667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3594 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5     10        30\n",
       "0    2010-01-04  19.79  19.80  19.36  19.41     NaN    NaN       NaN\n",
       "1    2010-01-05  19.33  19.50  18.90  19.08     NaN    NaN       NaN\n",
       "2    2010-01-06  19.04  19.68  18.91  19.67     NaN    NaN       NaN\n",
       "3    2010-01-07  19.50  19.64  18.00  18.63     NaN    NaN       NaN\n",
       "4    2010-01-08  18.63  18.69  18.19  18.43  19.044    NaN       NaN\n",
       "...         ...    ...    ...    ...    ...     ...    ...       ...\n",
       "3589 2024-12-19   5.41   5.42   5.33   5.41   5.498  5.570  5.527000\n",
       "3590 2024-12-20   5.41   5.42   5.37   5.37   5.444  5.544  5.517667\n",
       "3591 2024-12-23   5.38   5.40   5.30   5.31   5.394  5.515  5.507333\n",
       "3592 2024-12-24   5.31   5.41   5.31   5.40   5.384  5.495  5.500333\n",
       "3593 2024-12-25   5.40   5.42   5.32   5.35   5.368  5.466  5.491667\n",
       "\n",
       "[3594 rows x 8 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0           5.40   5.42\n",
       "1           5.31   5.41\n",
       "2           5.38   5.40\n",
       "3           5.41   5.42\n",
       "4           5.41   5.42\n",
       "...          ...    ...\n",
       "3589       18.63  18.69\n",
       "3590       19.50  19.64\n",
       "3591       19.04  19.68\n",
       "3592       19.33  19.50\n",
       "3593       19.79  19.80\n",
       "\n",
       "[3594 rows x 2 columns]>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19.41],\n",
       "       [19.08],\n",
       "       [19.67],\n",
       "       ...,\n",
       "       [ 5.31],\n",
       "       [ 5.4 ],\n",
       "       [ 5.35]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>19.79</td>\n",
       "      <td>19.80</td>\n",
       "      <td>19.36</td>\n",
       "      <td>19.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>19.33</td>\n",
       "      <td>19.50</td>\n",
       "      <td>18.90</td>\n",
       "      <td>19.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>19.04</td>\n",
       "      <td>19.68</td>\n",
       "      <td>18.91</td>\n",
       "      <td>19.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>19.50</td>\n",
       "      <td>19.64</td>\n",
       "      <td>18.00</td>\n",
       "      <td>18.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>18.63</td>\n",
       "      <td>18.69</td>\n",
       "      <td>18.19</td>\n",
       "      <td>18.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>5.41</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>5.41</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>5.38</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>5.31</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>5.40</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3594 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14613.0  19.79  19.80  19.36  19.41\n",
       "14614.0  19.33  19.50  18.90  19.08\n",
       "14615.0  19.04  19.68  18.91  19.67\n",
       "14616.0  19.50  19.64  18.00  18.63\n",
       "14617.0  18.63  18.69  18.19  18.43\n",
       "...        ...    ...    ...    ...\n",
       "20076.0   5.41   5.42   5.33   5.41\n",
       "20077.0   5.41   5.42   5.37   5.37\n",
       "20080.0   5.38   5.40   5.30   5.31\n",
       "20081.0   5.31   5.41   5.31   5.40\n",
       "20082.0   5.40   5.42   5.32   5.35\n",
       "\n",
       "[3594 rows x 4 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>19.79</td>\n",
       "      <td>19.80</td>\n",
       "      <td>19.36</td>\n",
       "      <td>19.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>19.33</td>\n",
       "      <td>19.50</td>\n",
       "      <td>18.90</td>\n",
       "      <td>19.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>19.04</td>\n",
       "      <td>19.68</td>\n",
       "      <td>18.91</td>\n",
       "      <td>19.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>19.50</td>\n",
       "      <td>19.64</td>\n",
       "      <td>18.00</td>\n",
       "      <td>18.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>18.63</td>\n",
       "      <td>18.69</td>\n",
       "      <td>18.19</td>\n",
       "      <td>18.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3589</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3590</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3591</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3592</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3593</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3594 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14613.0  19.79  19.80  19.36  19.41\n",
       "1     14614.0  19.33  19.50  18.90  19.08\n",
       "2     14615.0  19.04  19.68  18.91  19.67\n",
       "3     14616.0  19.50  19.64  18.00  18.63\n",
       "4     14617.0  18.63  18.69  18.19  18.43\n",
       "...       ...    ...    ...    ...    ...\n",
       "3589  20076.0   5.41   5.42   5.33   5.41\n",
       "3590  20077.0   5.41   5.42   5.37   5.37\n",
       "3591  20080.0   5.38   5.40   5.30   5.31\n",
       "3592  20081.0   5.31   5.41   5.31   5.40\n",
       "3593  20082.0   5.40   5.42   5.32   5.35\n",
       "\n",
       "[3594 rows x 5 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b117cd95b0>]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\computer\\anaconda\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.1146 - val_loss: 0.0301\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0102 - val_loss: 0.0024\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0018 - val_loss: 0.0031\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0014 - val_loss: 0.0013\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0012 - val_loss: 6.5676e-04\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0012 - val_loss: 4.4337e-04\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 3.6583e-04\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 2.8900e-04\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 2.2283e-04\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 1.6400e-04\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 1.3513e-04\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 1.1025e-04\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 8.6599e-05\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.9073e-04 - val_loss: 6.8352e-05\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.7010e-04 - val_loss: 5.8014e-05\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.4845e-04 - val_loss: 5.2049e-05\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.2649e-04 - val_loss: 4.9379e-05\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.0452e-04 - val_loss: 4.9365e-05\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.8367e-04 - val_loss: 4.9045e-05\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.6579e-04 - val_loss: 5.5197e-05\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.4154e-04 - val_loss: 5.9532e-05\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.2147e-04 - val_loss: 6.6462e-05\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.0168e-04 - val_loss: 7.6276e-05\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.8317e-04 - val_loss: 9.0244e-05\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.6605e-04 - val_loss: 1.0623e-04\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.5176e-04 - val_loss: 1.2502e-04\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.4027e-04 - val_loss: 1.4267e-04\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3169e-04 - val_loss: 1.5214e-04\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.2593e-04 - val_loss: 1.5803e-04\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.2171e-04 - val_loss: 1.6040e-04\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.06384\n",
      "均方根误差: 0.25266\n",
      "平均绝对误差: 0.21883\n",
      "R2: -0.10377\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
